System for non-invasive measurement of glucose in humans

ABSTRACT

An apparatus and method for non-invasive measurement of glucose in human tissue by quantitative infrared spectroscopy to clinically relevant levels of precision and accuracy. The system includes six subsystems optimized to contend with the complexities of the tissue spectrum, high signal-to-noise ratio and photometric accuracy requirements, tissue sampling errors, calibration maintenance problems, and calibration transfer problems. The six subsystems include an illumination subsystem, a tissue sampling subsystem, a calibration maintenance subsystem, an FTIR spectrometer subsystem, a data acquisition subsystem, and a computing subsystem.

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.09/832,586 entitled “Illumination Device and Method for SpectroscopicAnalysis”; U.S. patent application Ser. No. 09/832,608, entitled“Optically Similar Reference Samples and Related Methods forMultivariate Calibration Models Used in Optical Spectroscopy”; and U.S.patent application Ser. No. 09/832,631, entitled “Encoded VariableFilter Spectrometer”, all filed on the same date herewith and assignedto the assignee of the present application. The disclosure of each ofthese related applications is hereby incorporated by reference.

TECHNICAL FIELD

The present invention generally relates to a quantitative spectroscopysystem for measuring analyte concentrations or other attributes oftissue utilizing non-invasive techniques in combination withmultivariate analysis. More specifically, the present invention relatesto a quantitative near-infrared spectroscopy system, incorporatingmultiple subsystems in combination, providing precision and accuracy tomeasure analytes such as glucose at clinically relevant levels in humantissue.

BACKGROUND OF THE INVENTION

The non-invasive measurement of substances in the human body byquantitative spectroscopy has been found to be highly desirable, yetvery difficult to accomplish. Non-invasive measurements via quantitativespectroscopy are desirable because they are painless, do not require afluid draw from the body, carry little risk of contamination orinfection, do not generate any hazardous waste and have shortmeasurement times. A prime example of a desirable application of suchtechnology is the non-invasive measurement of blood glucose levels indiabetic patients, which would greatly improve diabetes treatment. U.S.Pat. No. 5,379,764 to Barnes et al. discloses the necessity fordiabetics to frequently monitor blood glucose levels. The more frequentthe blood glucose levels are measured, the less likely the occurrence oflarge swings in blood glucose levels. These large swings are associatedwith the very undesirable short-term symptoms and long-termcomplications of diabetes. Such long-term complications include heartdisease, arteriosclerosis, blindness, stroke, hypertension, kidneyfailure and premature death.

Several systems have been proposed for the non-invasive measurement ofblood glucose levels. These systems have included technologiesincorporating polarimetry, mid-infrared spectroscopy, Ramanspectroscopy, Kromoscopy, fluorescence spectroscopy, nuclear magneticresonance spectroscopy, radio-frequency spectroscopy, ultrasound,transdermal measurements, photoacoustic spectroscopy and near-infraredspectroscopy. However, despite these efforts, direct and invasivemeasurements (e.g., blood sampling by a lancet cut into the finger) arestill necessary for most, if not all, presently FDA approved andcommercially available glucose monitors. Because invasive measurementsare painful, inconvenient and costly to the diabetic patient,sufficiently frequent blood glucose measurement, which is necessary toensure effective diabetes management, is rarely achieved.

Of particular interest to the present invention are prior art systemswhich incorporate or generally utilize quantitative infraredspectroscopy as a theoretical basis for the analysis. In general, thesemethods involve probing glucose-containing tissue using infraredradiation in absorption or diffuse reflectance mode. It is known thatglucose absorbs at multiple frequencies in both the mid- andnear-infrared range. There are, however, other infrared active analytesin the tissue and blood that also absorb at similar frequencies. Due tothe overlapping nature of these absorption bands, no single or specificfrequency can be used for reliable non-invasive glucose measurement.Analysis of spectral data for glucose measurement thus requiresevaluation of many intensities over a wide spectral range to achieve thesensitivity, precision, accuracy, and reliability necessary forquantitative determination.

For example, Robinson et al. in U.S. Pat. No. 4,975,581 disclose amethod and apparatus for measuring a characteristic of unknown value ina biological sample using infrared spectroscopy in conjunction with amultivariate model that is empirically derived from a set of spectra ofbiological samples of known characteristic values. The above-mentionedcharacteristic is generally the concentration of an analyte, such asglucose, but also may be any chemical or physical property of thesample. The method of Robinson et al. involves a two-step process thatincludes both calibration and prediction steps.

In the calibration step, the infrared light is coupled to calibrationsamples of known characteristic values so that there is attenuation ofat least several wavelengths of the infrared radiation as a function ofthe various components and analytes comprising the sample with knowncharacteristic value. The infrared light is coupled to the sample bypassing the light through the sample or by reflecting the light off thesample. Absorption of the infrared light by the sample causes intensityvariations of the light that are a function of the wavelength of thelight. The resulting intensity variations at a minimum of severalwavelengths are measured for the set of calibration samples of knowncharacteristic values. Original or transformed intensity variations arethen empirically related to the known characteristics of the calibrationsamples using multivariate algorithms to obtain a multivariatecalibration model. The model preferably accounts for subjectvariability, instrument variability and environment variability.

In the prediction step, the infrared light is coupled to a sample ofunknown characteristic value, and a multivariate calibration model isapplied to the original or transformed intensity variations of theappropriate wavelengths of light measured from this unknown sample. Theresult of the prediction step is the estimated value of thecharacteristic of the unknown sample. The disclosure of Robinson et al.is incorporated herein by reference.

A further method of building a calibration model and using such modelfor prediction of analytes and/or attributes of tissue is disclosed incommonly assigned U.S. Pat. No. 6,157,041 to Thomas et al., entitled“Method and Apparatus for Tailoring Spectrographic Calibration Models,”the disclosure of which is incorporated herein by reference.

In “Near-Infrared Spectroscopy for Non-invasive Monitoring ofMetabolites”, Clinical Chemistry Lab Med 2000, 38(2): 137-145, 2000,Heise et al. disclose the non-invasive measurement of glucose in theinner lip of a subject utilizing a Fourier transform infrared (FTIR)spectrometer and a diffuse reflectance accessory. The instrument usedfor this measurement contained a tungsten light source with an outputthat was collimated and sent into a Bruker IFS-66 FTIR spectrometer. TheFTIR spectrometer modulated the light in a manner that created aninterferogram and the collimated interferogram was sent to a diffusereflectance accessory. The diffuse reflectance accessory was abifurcated, Y-shaped fiber optic probe. The input fibers of the proberadiated the inner lip of a subject or a spectralon reference standardwith the interferogram from the FTIR spectrometer. Light diffuselyreflected from the inner lip was collected by the output fibers of thediffuse reflectance accessory and focused onto a liquid nitrogen cooledInSb detector. The optical interferograms were converted to anelectrical signal by the InSb detector and the electrical signal wasdigitized by an analog-to-digital converter (ADC). The digitizedinterferogram was then converted into an NIR spectrum and a collectionof these spectra and corresponding blood glucose reference values werecorrelated using multivariate techniques to produce a calibration fornon-invasive glucose measurements. This instrument was able to producecross-validated, leave-one-out-at-a-time glucose standard error ofpredictions (SEP) of 36.4 mg/dl. This level of accuracy and precision isnot of clinical utility.

In “Near-Infrared Spectrometric Investigation of Pulsatile Blood Flowfor Non-Invasive Metabolite Monitoring”, CP430, Fourier TransformSpectroscopy: 11^(th) International Conference, 1998, Heise et al.discuss the non-invasive measurement of glucose in the inner lip of asubject using multivariate analysis of spectra with pulsatile bloodflow. Heise et al. assert that by taking the difference between thesystolic and diastolic portions of the cardiac cycle, interferences canbe removed and glucose predictions are done on the spectra due to theadditional blood volume. The optical pathlength due to the additionalblood volume is 50 to 70 times shorter than an integrated NIRmeasurement, resulting in a dramatically reduced glucose signal-to-noiseratio (SNR). Heise used the instrument described in the precedingparagraph to make his measurements. No glucose prediction results weredisclosed.

In “Spectroscopic and Clinical Aspects of Non-invasive GlucoseMeasurements”, Clinical Chemistry, 45:2, 165-177, 1999, Khalil gives anoverview of non-invasive glucose monitoring techniques. Khalil coversNIR transmission and reflectance, mechanical manipulation of the tissuecoupled with NIR spectroscopy, Kromoscopy, spatially resolved diffusereflectance, frequency domain measurements, polarimetry measurements,Raman spectroscopy and photo-acoustic methods.

In U.S. Pat. No. 5,361,758, Hall et al. describe a method and apparatusfor the non-invasive measurement of glucose. This device is composed ofa broadband light source, transfer optics from the light source to thesampling accessory, a tissue sampling accessory, transfer optics fromthe tissue sampling accessory to a dispersive spectrometer whose mainoptical elements are a diffraction grating and a linear array detectorand finally processing and display subsystems. Hall et al. disclosetaking the second derivative of the NIR absorbance spectrum collected bythe above instrument and applying a calibration model to the secondderivative of the absorbance spectrum to predict glucose concentrations.

In U.S. Pat. No. 5,743,262, Lepper, Jr. et al. describe a method andapparatus for the non-invasive measurement of glucose. This device iscomposed of a broadband light source, a collimating optic for the lightsource, an optical filter for modulating the output of the light source,a tissue sampling accessory, a photodetector, a data acquisitionsubsystem and a signal processing subsystem. The optical filter passesselect wavelengths of light from the broadband source in a given timeinterval. The selected wavelength of light is sent into thetissue-sampling accessory to irradiate the tissue. Light collected fromthe tissue is focused onto a detector, and the electrical signal outputfrom the detector is digitized by an analog-to-digital converter. Thesignal processing subsystem takes a “double log” transformation of thesignal and then uses the result to predict glucose concentrations.

In U.S. Pat. No. 5,750,994, Schlager describes a method and apparatusfor non-invasive measurement of glucose in the NIR range using opticaltransfer cells that have positive correlation filters that are selectivefor the analyte of interest. This apparatus includes a dispersivespectrometer along with a broadband light source, a tissue-samplingaccessory, a detector or linear array detector and a data acquisitionsubsystem.

In U.S. Pat. No. 5,830,112, Robinson describes a general method ofrobust sampling of tissue for non-invasive analyte measurement. Thesampling method utilizes a tissue-sampling accessory that is pathlengthoptimized by spectral region for measuring an analyte such as glucose.The patent discloses several types of spectrometers for measuring thespectrum of the tissue from 400 to 2500 nm, including acousto-opticaltunable filters, discrete wavelength spectrometers, filters, gratingspectrometers and FTIR spectrometers. The disclosure of Robinson isincorporated hereby reference.

In U.S. Pat. No. 6,016,435, Maruo et al. describe an apparatus for thenon-invasive measurement of glucose. This device uses a broadband lightsource coupled to a stepped grating monochrometer to generate successivewavelengths of light in the NIR spectral region. The output of themonochrometer is sent to an optical fiber bundle that samples the tissueof a subject. The optical fiber bundle radiates the skin with the lightfrom the monochrometer and collects diffusely reflected light from theskin of the subject. The collected diffuse reflectance spectrum is sentto a detector and the electrical signal from the detector is digitized.An absorbance spectrum is generated from the digitized output of thedetector and that diffuse reflectance spectrum is used to make aprediction of glucose concentration.

In U.S. Pat. No. 6,026,314, Amerov et al. describe a method andapparatus for the non-invasive measurement of glucose that utilizespulsed, discrete wavelengths of light in the NIR spectral region. Thepulsed light source may be a flash lamp, light emitting diodes or laserdiodes. The output of the pulsed light source is coupled to atissue-sampling accessory that utilizes prisms or fiber optics toirradiate the tissue and collect absorbance spectra from the tissue. Theoutput of the sampling accessory is sent to one or more detectors whichconvert the optical signal to an electrical signal. The electricalsignals from the detectors are amplified and undergo analog-to-digitalconversion. The digitized signals are then processed, and an algorithmis applied to predict glucose concentration.

In U.S. Pat. No. 6,049,727, Crothall describes an implanted glucosesensing system that measures glucose in vivo and is meant to couple toan insulin pump to create an artificial pancreas. The implanted sensoruses a number of discrete wavelengths which irradiate a blood vessel.The light is absorbed and scattered by the blood and tissue in theoptical path between the light sources and the detector. The detectedlight is converted from an optical signal to an electrical signal, andthen digitized by an analog-to-digital converter. The digitized signalis sent to a radio frequency transceiver which communicates with anexternal processing system to apply an algorithm to the digitizedabsorbance spectrum to calculate glucose concentration. The resultingglucose concentration information is utilized to control theadministration of insulin to the subject by an insulin pump. This closedloop system is meant to create an artificial pancreas for insulindependent diabetics.

In U.S. Pat. No. 6,061,582, Small et al. describe a method and apparatusfor non-invasive determination of glucose. The apparatus for themeasurement includes a broadband light source, an FTIR spectrometer,tissue sampling accessory, a detector and data acquisition system and aprocessing system. The spectra collected from the subject are digitallyfiltered to isolate a portion of the spectrum due to the glucose signal.Multivariate analysis techniques are then applied to the digitallyfiltered spectrum to generate a glucose prediction. The tissue-samplingaccessory can collect spectra from the subject using transmission ordiffuse reflectance.

In PCT Application, WO 99/43255, Small et al. describe a non-invasiveglucose monitoring apparatus and method that measures glucose bytransmission of NIR light through the tongue of a subject. The apparatusfor the measurement includes a broadband light source, an FTIRspectrometer, tissue sampling accessory, a detector and data acquisitionsystem and a processing system. The prediction results presented in thisapplication do not achieve the levels of precision and accuracynecessary for clinical application.

In “New Approach to High-Precision Fourier Transform SpectrometerDesign”, Applied Optics, 35:16, 2891-2895, 1996, Brault introduces aconstant time sampling analog-to-digital conversion technique for FTIRspectrometers that allows use of high dynamic range delta-sigma ADCs.Brault asserts their approach provides a superior technique forimplementing the data acquisition system of an FTIR spectrometer becauseit avoids the artifacts of gain ranging and the need to precisely matchthe time delays between the laser reference and infrared measurementchannels. In “Uniform Time-Sampling Fourier Transform Spectroscopy”,Applied Optics, 36:1-, 2206-2210, 1997, Brasunas et al. discuss avariation of Brault's constant time sampling analog-to-digitalconversion technique for FTIR spectrometers.

In U.S. Pat. No. 5,914,780, Turner et al. describe a method ofdigitizing the interferogram of an FTIR spectrometer using a constanttime sampling analog-to-digital converter. The constant time samplingtechnique allows the use of high dynamic range, delta-sigmaanalog-to-digital converters that obviate the need for gain rangingcircuitry and precisely matched delays between the reference laser andinfrared signals. This type of data acquisition system is asserted toprovide the FTIR spectrometer with higher SNR and superior photometricaccuracy when compared to the previously employed sampling techniquewhich is triggered by the zero crossings of the reference laser.

Although there has been substantial work conducted in attempting toproduce a commercially viable non-invasive near-infraredspectroscopy-based glucose monitor, no such device is presentlyavailable. It is believed that prior art systems discussed above havefailed for one or more reasons to fully meet the challenges imposed bythe spectral characteristics of tissue which make the design of anon-invasive measurement system a formidable task. Thus, there is asubstantial need for a commercially viable device which incorporatessubsystems and methods with sufficient accuracy and precision to makeclinically relevant measurements of analytes, such as glucose, in humantissue.

SUMMARY OF THE INVENTION

The present invention is directed to an apparatus and method for thenon-invasive measurement of glucose in human tissue by quantitativeinfrared spectroscopy to clinically relevant levels of precision andaccuracy. A clinically relevant level of precision and accuracy isdefined as the measurement of glucose concentration in humans to a levelof precision and accuracy such that a patient can base insulin dosingand/or diet modification on the glucose concentration measurement madeby the noninvasive device. In addition, the noninvasive measurement hassufficient accuracy and precision such that either hypo-glycemia orhyper-glycemia can be diagnosed.

A Clark Error Grid provides a means to measure the clinical relevance ofmeasurements on a system as compared to a reference measurement formeasurements made over a period of time. With the present systemacceptable, preferred and ideal Clark Error Grid data have been defined,each providing clinically relevant glucose measurements. An acceptablesystem includes a plot with 72% or greater in Region A, 24% or less inRegion B. 1% or less in Region C, 3% or less in Region D and about 0% inRegion E. A preferred system includes a plot with 85% or greater inRegion A, 14.4% or less in Region B, 0.1% or less in Region C, 0.5% orless in Region D and about 0% in Region E. An ideal system includes aplot with 98.5% or greater in Region A, 1.5% or less in Region B andabout 0% in Regions C, D and E. In one preferred system of the presentinvention, 80% or more predictions on a single subject within aphysiological range of glucose fall in Region A of a Clark Error Grid.In this embodiment, it is also preferred that 18.5% or less fall inRegion B, about 0% in Region C, 1.5% or less in Region D and about 0% inRegion E.

If glucose concentration measurements are taken in a more continuousmanner instead of several discrete measurements per day, therequirements for accuracy and precision can be relaxed and stillmaintain clinical relevance. It is recognized that the minimum thresholdfor percentage of measurements in each of the regions of the Clark ErrorGrid can depend on the peculiarities of the way the noninvasivemeasurement is taken, differences between individual subjects and/orother circumstances. The preferred standard for the noninvasive glucosemeasurement of the present invention is that it must allow the user toeffectively maintain glycemic control and avoid either hypo-glycemic orhyper-glycemic conditions.

The present system overcomes the challenges posed by the spectralcharacteristics of tissue by incorporating a design which includes, inpreferred embodiments, six highly optimized subsystems. The designcontends with the complexities of the tissue spectrum, highsignal-to-noise ratio and photometric accuracy requirements, tissuesampling errors, calibration maintenance problems, calibration transferproblems plus a host of other issues. The six subsystems include anillumination subsystem, a tissue sampling subsystem, a calibrationmaintenance subsystem, an FTIR spectrometer subsystem, a dataacquisition subsystem, and a computing subsystem.

The present invention further includes apparatus and methods which allowfor implementation and integration of each of these subsystems in orderto ensure that the glucose net analyte signal-to-noise ratio ispreserved to the maximum amount. The glucose net analyte signal is theportion of the near-infrared spectrum that is specific for glucoseconcentration levels because it is orthogonal to all other sources ofspectral variance. The orthogonal nature of the glucose net analytesignal makes it perpendicular to the space defined by any interferingspecies and as a result, the net analyte signal is uncorrelated to thesesources of variance. The glucose net analyte signal-to-noise ratio isdirectly related to the accuracy and precision of the present inventionfor non-invasive measurement of glucose by quantitative near-infraredspectroscopy.

The present invention preferably utilizes near-infrared radiation foranalysis. Applicants have found that radiation in the wavelength rangeof 1.2 to 2.5 microns (or frequency range of 8000 to 4000 cm⁻¹) is ofprime interest for making the present non-invasive measurements becausesuch radiation has acceptable specificity for a number of analytes,including glucose, along with tissue optical penetration depths of up to5 millimeters with acceptable absorbance characteristics. In the 1.2 to2.5 micron spectral region, the large number of optically activesubstances that make up the tissue complicate the measurement of anygiven substance due to the overlapped nature of their absorbancespectra. Applicants have utilized herein multivariate analysistechniques which are believed required to resolve these overlappedspectra such that accurate measurements of the substance of interest canbe achieved. Use of multivariate analysis techniques, however, alsorequires the maintenance and transfer of multivariate calibrations,which techniques Applicants have developed in order to accuratelymeasure analytes, especially when trying to measure these analytes asweak absorbers found in the presence of much stronger absorbers, such aswater.

A typical prior art non-invasive measurement system will have anillumination subsystem which generates the near-infrared light, a tissuesampling accessory which irradiates and collects light from the tissue,a spectrometer, a data acquisition subsystem, a reference device forcalibration maintenance and a processing unit. Each of these subsystemshas significant influence on the accuracy of the non-invasivemeasurement. The present invention documents a multidisciplinaryapproach to the design of the present instrument which incorporates anunderstanding of the instrument subsystems, tissue physiology,multivariate analysis, near-infrared spectroscopy and overall systemoperation. Further, the interactions between the subsystems have beenanalyzed so that the behavior and requirements for the entirenon-invasive measurement device are well understood and result in adesign for a commercial instrument that will make non-invasivemeasurements with sufficient accuracy and precision at a price and sizethat is commercially viable.

The subsystems of the non-invasive glucose monitor are highly optimizedto provide reproducible and, preferably, uniform radiance of the tissue,low tissue sampling error, depth targeting of the glucose-bearing layersof the tissue, efficient collection of diffuse reflectance spectra fromthe tissue, high optical throughput, high photometric accuracy, largedynamic range, excellent thermal stability, effective calibrationmaintenance, effective calibration transfer, built-in quality controland ease-of-use. All of these factors have been considered as importantto maximize the glucose net analyte signal-to-noise ratio and provideclinically relevant levels of prediction accuracy and precision for theadministration of insulin therapy and other therapies related to thedetection and management of diabetes. The present invention has beenfound to provide clinically relevant levels of glucose prediction andaccuracy over a minimum of two months for a diverse subject population.With the present system, the overall standard error of prediction for 40subjects over a 7-week validation study was 21.7 mg/dl. Further, asshown in FIG. 56, 83.5% of the data was within section “A” of a ClarkError Grid, 15.4% in section “B”, 0% in section “C”, 1.1% in section “D”and 0% in section “E”.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic depiction of a non-invasive spectrometer systemincorporating the subsystems of the present invention;

FIG. 2 is a detailed perspective view of an infrared radiation sourcelamp known in the art;

FIG. 3 is a diagramed view of a system for measuring the concentrationof an analyte within biological tissue;

FIG. 4a is an incidance plot using a ray trace program simulating thespatial distribution of emitted radiation from an infraredspectrophotometer known in the art;

FIG. 4b is an incidance plot showing the changes in spatial distributionof emitted radiation after a 90-degree rotation of the filanent used inproducing the incidance plot of FIG. 4a;

FIG. 4c is an incidance plot showing the changes in spatial distributionof emitted radiation after a one-millimeter vertical translation of thefilament used in producing the incidance plot of FIG. 4a;

FIG. 5a is an intensity plot using a ray trace program simulating theangular distribution of emitted radiation from an infraredspectrophotometer known in the art;

FIG. 5b is an intensity plot showing the changes in angular distributionof emitted radiation after a 90-degree rotation of the filament used inproducing the intensity plot of FIG. 5a;

FIG. 5c is an intensity plot showing the changes in angular distributionof emitted radiation after a one-millimeter vertical translation of thefilament used in producing the intensity plot of FIG. 5a;

FIG. 6 is a diagramed view of a system for constructing a chemometricmodel for measuring glucose concentration in the forearm's of varioussubjects;

FIG. 7 is a box and whisker plot of prediction error versus day acrossfive lamp changes using the system illustrated in FIG. 6;

FIG. 8 is a box and whisker plot of in-vivo prediction errors versusorientation for a lamp within a system illustrated in FIG. 6;

FIG. 9 is a diagramed view of a system used for cross-validationanalysis for baseline system performance using a tissue phantom for thesample source;

FIG. 10a is a box and whisker plot of cross-validated prediction errorsfor the system illustrated in FIG. 9, in the absence of a lamp change;

FIG. 10b is a box and whisker plot of cross-validated prediction errorsfor the system illustrated in FIG. 9, with the inclusion of lampchanges;

FIG. 11 is a diagramed view of a system of the present invention using ameans for spatially and angularly homogenizing emitted radiation;

FIG. 12a and FIG. 12b are depicted as a perspective and a plan view of alight pipe of the present invention;

FIG. 13 is a plan view of a ray trace showing radiation focused by anelliptical reflector into and through a light pipe of the presentinvention;

FIG. 14a is an incidance plot using a ray trace program simulating thespatial distribution of emitted radiation from an infraredspectrophotometer using a light pipe of the present invention;

FIG. 14b is an incidance plot showing the changes in spatialdistribution of emitted radiation after a 90-degree rotation of thefilament used in producing the incidance plot of FIG. 14a;

FIG. 14c is an incidance plot showing the changes in spatialdistribution of emitted radiation after a one-millimeter verticaltranslation of the filament used in producing the incidance plot of FIG.14a;

FIG. 15a is an intensity plot using a ray trace program simulating theangular distribution of emitted radiation from an infraredspectrophotometer using a light pipe of the present invention;

FIG. 15b is an intensity plot showing the changes in angulardistribution of emitted radiation after a 90-degree rotation of thefilament used in producing the intensity plot of FIG. 15a;

FIG. 15c is an intensity plot showing the changes in angulardistribution of emitted radiation after a one-millimeter verticaltranslation of the filament used in producing the intensity plot of FIG.15a;

FIG. 16 is a schematic plan view of an alternative source and light pipesystem of the present invention;

FIG. 17 is an incidance plot depicting homogenization of the light atthe distal end of the light pipe of FIG. 16;

FIG. 18 is an intensity plot showing the homogenization of light emittedfrom the light pipe of FIG. 16;

FIG. 19 is a schematic plan view of an alternative illumination sourceincorporating parabolic reflectors and a light pipe;

FIG. 20 is an incidance plot depicting spatial homogenization of thelight;

FIG. 21 is a plot of intensity showing the homogenization of light bythe source in FIG. 19;

FIG. 22 is a schematic perspective view of an alternative illuminationsource incorporating faceted reflectors;

FIG. 23 depicts spatial distribution of the light showing spatialhomogenization achieved through the system of FIG. 22;

FIG. 24 is a plot of angular distribution produced by the device of FIG.22;

FIG. 25 is a diagramed view of a system of the present invention formeasuring glucose in scattering media having a tissue phantom as thesample source;

FIG. 26a is a box and whisker plot of a standard system with no bulbchanges;

FIG. 26b is a box and whisker plot of a standard system across four bulbchanges;

FIG. 26c is a box and whisker plot of a system using an s-bend lightpipe across four bulb changes;

FIG. 26d is a box and whisker plot of a system using a ground glassdiffuser plus and s-bend light pipe across four bulb changes;

FIG. 27 is a diagrammed view of a system incorporating filters prior tothe light pipe which eliminate unwanted wavelengths of radiation fromthe illumination source;

FIG. 28 graphically depicts the transmittance of selected wavelengths ina preferred fingerprint region;

FIG. 29 is a perspective view of elements of a preferred tissue samplingsubsystem;

FIG. 30 is a plan view of the sampling surface of the tissue samplingsubsystem of FIG. 29, showing a preferred arrangement of input andoutput optical fiber ends;

FIG. 31 is a perspective view of a preferred ergonomic apparatus forholding the sampling surface and positioning a tissue surface thereon;

FIG. 32 is a simplified schematic view of an FTIR spectrometer utilizedin a subsystem of the present invention;

FIG. 33 depicts a typical interferogram created by the spectrometer ofFIG. 32;

FIG. 34 shows two graphs of spectral residuals comparing a conventionalair background to a similar background;

FIG. 35 shows a graph of standard error of prediction comparing nobackground, a conventional air background, and a similar background inthe presence of instrument and environmental variation;

FIG. 36 shows a graph of the spectral differences between the mean humantissue spectrum and two different backgrounds, namely a conventional airbackground and a similar background;

FIG. 37 is a flowchart illustrating the steps used in quantifyingspectral similarity;

FIGS. 38 and 39 illustrate a cone background device in accordance withan embodiment of the present invention, wherein FIG. 38 illustrates aray-trace of the cone background device and FIG. 39 illustrates apartial cut-away view of the cone background device;

FIG. 40 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the cone background;

FIG. 41 schematically illustrates a scattering solution background inaccordance with an embodiment of the present invention;

FIG. 42 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the scattering solution background;

FIG. 43 schematically illustrates a roof background in accordance withan embodiment of the present invention;

FIG. 44 schematically illustrates an alternative roof background aspositioned on a fiber optic sampling array;

FIG. 45 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the roof background;

FIG. 46 schematically illustrates a multi-layer background in accordancewith an embodiment of the present invention;

FIG. 47 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the multi-layered background;

FIG. 48 schematically illustrates a transmission cell background inaccordance with an embodiment of the present invention;

FIG. 49 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the transmission cell background;

FIG. 50 schematically illustrates a variable height temporal backgroundin accordance with an embodiment of the present invention;

FIG. 51 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the variable height temporalbackground;

FIG. 52 schematically illustrates a collagen gel matrix background inaccordance with an embodiment of the present invention;

FIG. 53 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the collagen gel matrix background;

FIG. 54 schematically illustrates an animal tissue (bovine) backgroundin accordance with an embodiment of the present invention;

FIG. 55 shows a graph of spectral response demonstrating the spectralmatch between the tissue sample and the bovine tissue background;

FIG. 56 is a Clark Error Grid which graphically depicts experimentalresults showing the ability of the system of the present invention toderive clinically relevant glucose measurements in tissue on numeroussubjects over two months; and

FIG. 57 is a graphical depiction of the concept of net analyte signal ina three-component system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following detailed description should be read with reference to thedrawings in which similar elements in different drawings are numberedthe same. The drawings, which are not necessarily to scale, depictillustrative embodiments that are not intended to limit the scope of theinvention.

Referring now to FIG. 1, a non-invasive glucose monitor system that isable to achieve clinically relevant levels of accuracy and precision isdepicted in schematic view. The overall system includes six subsystems.The subsystems include an illumination subsystem 100, a tissue samplingsubsystem 200, a calibration maintenance subsystem 300, an FTIRspectrometer subsystem 400, a data acquisition subsystem 500 and anembedded processing subsystem 600. The subsystems have been designed andcarefully integrated in order to ensure that the glucose net analytesignal-to-noise ratio is preserved to the maximum amount. The glucosenet analyte signal is the portion of the near-infrared spectrum that isspecific for glucose concentration levels because it is orthogonal toother sources of spectral variance. The glucose net analytesignal-to-noise ratio is directly related to the accuracy and precisionof the non-invasive measurement of glucose by quantitative near-infraredspectroscopy with the present invention.

The subsystems provide reproducible and preferably uniform radiance ofthe tissue, low tissue sampling error, depth targeting of theglucose-bearing layers of the tissue, efficient collection of diffusereflectance spectra from the tissue, high optical throughput, highphotometric accuracy, large dynamic range, excellent thermal stability,effective calibration maintenance, effective calibration transfer,built-in quality control and ease-of-use. Each of these factors areoptimized to maximize the glucose net analyte signal-to-noise ratiowhich results in clinically relevant levels of glucose predictionaccuracy for insulin therapy. Each of the subsystems is discussed belowin detail followed by experimental evidence documenting that a preferredembodiment of the present system provides clinically relevant levels ofprecision and accuracy in glucose analysis in tissue.

The illumination subsystem 100 generates the near-infrared (NIR) lightused to interrogate the tissue of a human for the non-invasivemeasurement of glucose. The illumination subsystem, in an exemplaryembodiment, contains a broadband, polychromatic light source 14 thatemits radiation in the NIR portion of the spectrum. The light source 14may emit radiation outside of the NIR, also. An example of a suitablelight source 14 would be a 40-watt, 22.8-volt tungsten filament lamp.The light source 14 is typically driven by a tightly regulated powersupply. The power supply may supply the lamp with constant current,constant voltage or constant power. The power supply for the lightsource should provide tight regulation of current, voltage or power tokeep the color temperature and emissivity of the light source as stableas possible. Fluctuations of the light source's color temperature andemissivity are a source of noise in the non-invasive glucose measurementand can reduce the net analyte SNR and, subsequently, the accuracy andprecision of the measurement. In preferred embodiments, the overallsystem of the present invention includes a power supply which providesregulated, low noise power to all of the subsystems. The power supply ispreferably a 300-watt, quad output, resonant power, medical grade, ACpower to DC converter that provides output voltages of +28, +15, −15,and +5 VDC. The ripple on each of the voltages is less than 20millivolts peak-to-peak and the switching frequency of the supply isgreater than 200 kilohertz to facilitate additional filtering of thepower and to further reduce noise. Additionally, the power supply has aconversion efficiency of at least 80% which is important to reduce thethermal loading of the non-invasive monitor to the point that onlyconvection cooling is required for normal device operation. Theillumination subsystem 100 utilizes the 28 VDC power from the powersupply to drive the light source. A DC-to-DC converter tightly regulatesthe input power down to 21.4 VDC and also provides a soft start functionthat gradually turns on the light source when the non-invasive glucosemonitor is first turned on. The soft start function extends the usefullife of the light source by eliminating startup transients and limitingthe current required to initially power the light source.

In addition to the light source and regulated power supply, theillumination subsystem will contain optical elements 12,13,90 thatcollect the radiation from the light source and transfer that light tothe input of the tissue sampling subsystem. The elements that makeup thetransfer optics may include collimating and/or condensing optics,optical filters, optical diffusers, a homogenizer or light pipe forscrambling and the corresponding mechanical components to hold theoptics and light source.

The collimating optics may be refractive or reflective elements. Anexample of a refractive collimating optic would be a lens. An example ofa reflective collimating optic would be a parabolic mirror. Thecondensing optics may also be refractive or reflective. An example of arefractive condensing optic would be a lens. An example of a reflectivecondensing optic would be an elliptical mirror. The materials for lensesand mirrors are well known in the art. The reflective optics may have asmooth finish, a rough finish or a faceted finish depending on theconfiguration of the illumination subsystem. The purpose of the rough orfaceted finishes for the reflective optics is to destroy the coherenceof the light source image to create a more uniform radiance pattern. Therefractive optics can be spherical or aspherical. The Fresnel lens is aspecial type of aspherical lens that also may be employed. The purposeof the collimating and/or condensing optics is to collect radiation fromthe source and transfer the radiation to the input of the tissuesampling subsystem 200 or to other optical elements that performadditional operations on the light before it is passed to the tissuesampling subsystem 200.

One or more optical filters 13 may be employed to preferentially passradiation only in the spectral region of interest. The optical filtermay be one or a combination of long pass, short pass, or band passfilters. These filters can be absorptive, interference or dichroic innature. In some embodiments, the optical filters are anti-reflectioncoated to preserve the transmittance of light in the spectral region ofinterest. These filters may also perform spectral shaping of theradiation from the light source to emphasize certain portions of the NIRspectrum over others. The optical filtering is typically done tobandlimit the radiation impinging on the tissue to increase the SNR inthe region of interest and to keep from burning or otherwise damagingthe tissue of the subject. Bandlimiting the radiation improves theeffective SNR by reducing detector Shot noise that results from unwantedradiation outside the spectral region of interest.

The purpose of the optical diffusers 13 and scramblers 90 in theillumination subsystem is to provide reproducible and, preferably,uniform radiance at the input of the tissue sampling subsystem 200. Ithas been found that uniform radiance is necessary to ensure goodphotometric accuracy and even illumination of the tissue. Uniformradiance is also necessary to reduce errors associated withmanufacturing differences between light sources. Uniform radiance isutilized in the present invention for achieving accurate and precisenon-invasive glucose measurements.

An example of an optical diffuser is a ground glass plate. The groundsurface of the plate effectively scrambles the angle of the radiationemanating from the light source and its transfer optics. A light pipe isused to scramble the intensity of the radiation such that the intensityis uniform at the output of the light pipe. In addition, light pipeswith a double bend will scramble the angles of the radiation. Forcreation of uniform intensity and angular distribution, the crosssection of the light pipe should not be circular. Square, hexagonal andoctagonal cross sections are effective scrambling geometries. The outputof the light pipe may directly couple to the input of the tissue sampleror may be used in conjunction with additional transfer optics before thelight is sent to the tissue sampler. Exemplary illumination subsystemsare disclosed below and in commonly assigned U.S. patent applicationSer. No. 09/832,586, filed on the same date herewith and entitled“Illumination Device and Method for Spectroscopic Analysis”, thedisclosure of which is incorporated herein by reference.

FIG. 2 shows a plan view of an infrared radiation source lamp 14 knownin the art. The appearance of a radiant source lamp 14 closely resemblesthat of a traditional residential light bulb. Traditionalspectrophotometer lamps consist of a filament 16 housed within atransparent envelope 18, or the like. The transparent envelope 18 iseither comprised of a silicate glass, fused silica or quartz material.The material used for the glass envelope 18 is dependent upon thewavelength regions being surveyed on the electromagnetic spectrum.

The envelope 18 traditionally is cylindrical or oval in shape. The lamp14 of FIG. 2 specifically is of a closed-end cylindrical variety. Theclosed-end portion of the cylinder has a nipple 20 positioned near thecenter of the cylinder's closed-end base. The nipple 20 formation is aresult of manufacturing and functionally has no beneficial purpose. Onthe other hand, the nipple 20, as will be discussed in detail later,affects the emission of radiant energy.

Filament 16, and subsequently lamp choice, is wavelength dependent.Operating in the infrared and near infrared regions of theelectromagnetic spectrum requires a radiation source filament 16applicable to those spectral regions. Several continuous radiationsources including tungsten-halogen lamps, tungsten lamps, nerst glowers,nichrome wires and globars are suitable for infrared molecularabsorption spectroscopy. The desired filament is manufactured so as toplace the filament 16 within the open end of the glass envelope 18 andsecurely fastened thereto. Wires or leads 22 emerge from the filament 16and out of the glass envelope 18 attaching the filament 16 to an energysource (not shown). Because the energy output of a filament 16 generallyvaries approximately with the operating voltage, close voltage controlis essential. For this reason, most lamps 14 are attached through thewires or leads 22 to a constant-voltage transformer or electronicvoltage regulator.

The basic illumination source depicted in FIG. 2 further includes anelliptical reflector 12 which focuses emitted light from bulb 14 to areflector focus 26. Representative rays 24 are depicted to show thefunction of the reflector 12. The relationship between the radiantsource emitter 14 and the elliptical reflector 12 was used in thesubsequently disclosed experiments.

Referring now to FIG. 3, a diagramed view of a system 10 for measuringthe concentration of an analyte within biological tissue is depicted.The system 10 depicted is simplified by illustrating certain specificelements within a far more elaborate spectroscopic system. The elementsdepicted in FIG. 3, however, are common to spectroscopic systems, andtherefore, require some identification.

An elliptical reflector 12 known in the art is shown. At the center ofthe elliptical reflector 12 is radiation source lamp 14. The radiationsource lamp 14 is depicted as having a filament 16, a glass envelope 18with nipple 20 housing the filament 16, and a pair of leads 22 extendingfrom the end of the lamp. Surrounding a portion of the lamp 14 is thebody of the reflector 12. The elliptical reflector 12 functions toconcentrate emitted radiation rays 24 (shown as a ray trace) from theradiation source lamp 14 onto the reflector's focal point 26. In orderto maximize reflectance, the elliptical reflector 12 is generally madefrom a highly polished metal. Although FIG. 3 specifically illustratesan elliptical reflector, other shapes suitable for focusing radiantenergy are also within the scope of the invention.

FIG. 3 depicts two fiber optic bundles, an illumination fiber bundle 30and a collection fiber bundle 32. Fiber optic bundles 30 and 32 areextremely versatile because they are capable of channeling harnessedradiation between elements without noticeable reduction in the intensityof that harnessed radiation.

At the reflector's focal point 26 is an opening to the illuminationfiber bundle 30. The illumination fiber bundle 30 collects the radiationemitted 24 by the lamp 14 and channels the radiation through the bundlesystem. At the output end of the illumination fiber bundle 30 is anotheropening that then directs the harnessed radiation onto a sample 40, suchas human tissue on a person's forearm. The second fiber optic bundle,the collection fiber bundle 32, is positioned proximate to the sample 40to again collect radiation, however, here the radiation is diffuselyreflected from the sample 40.

Diffusely reflected radiation is that radiation reflected from withinthe sample 40. Diffusely reflected radiation does not generally follow auniform pattern. Ray tracing of the diffusely reflected radiation withinthe sample 40 as shown in FIG. 3 illustrates possible pathways ofradiation entering, and subsequently reflecting out of, the sample 40.

The collection fiber bundle 32 then channels the diffusely reflectedradiation from the sample 40 to the FTIR spectrometer subsystem 400 (seeFIG. 1). In FIG. 3, the FTIR spectrometer subsystem is shown simply asspectrophotometer 44 and is where the radiation is detected byconverting the recaptured radiant energy into a measurable signal. In apreferred embodiment of the invention, an FTIR spectrophotometer isutilized to analyze the diffusely reflected radiant energy emitted bythe sample 40. The output optical signal from the FTIR is focused on aphotodetector which converts the optical signal to an electrical signalthat is then transferred to the data acquisition subsystem 500 (seeFIG. 1) which includes a signal processor. Processing of the signal isgenerally accomplished using a computer or other data processing means46 designed for such processing. The outcome of the processing is thentranscribed to a readout, allowing practitioners to study the results ofthe analysis.

As described in detail above, in spectrophotometer instruments whereshot noise predominates the system, as is in the system depicted in FIG.3, the signal-to-noise ratio (SNR) for the system is directlyproportional to the square root of the flux (Φ) on the photodetector.The SNR for the system, however, can be improved by maximizing theamount of radiation incident on the detector. Increasing the flux on thedetector generally necessitates increasing the incidance, and thus, maycause thermal damage on the sampled biological tissue 40. To illustratethis tissue-heating problem, experiments were conducted utilizing thesystem illustrated in FIG. 3. For the experiment, the sample 40 used wasthe forearm of a living human subject and the analyte to be measured wasglucose.

The radiation source lamp 14 was connected to a variable current sourcethat permitted the lamp 14 to increase output up to a maximum of 40watts. The output of the lamp would then be incrementally increaseduntil the SNR was high enough to acquire accurate glucose measurements.As the lamp power was increased during the subsequent experimentaltrials, most of the subjects reported discomfort prior to reaching anacceptable SNR. The discomfort experienced by the subjects was due to alocalized heating of their forearm by the illuminating radiation.

To further analyze the above-described phenomenon, a ray trace programwas utilized to compare and contrast various illumination systems forspatial and angular homogeneity. TracePro V2.1, a commercially availablenon-sequential ray trace program, was used to generate realistic modelsof the radiation distributions from various illumination systemconfigurations. The output from such modeling is depicted in FIGS. 4a-c,5 a-c, 14 a-c and 15 a-c. In order to understand the output of themodeled illumination, Table 1 correlates the specific radiometric termsto their corresponding symbols, definitions, and units.

TABLE 1 Definition of Radiometric Quantities Name Symbol DefinitionUnits Energy Q — Joules, J Flux Φ $\frac{\partial Q}{\partial t}$

Watts, W Exitance M $\frac{\partial\Phi}{\partial A_{s}}$

W/m² Incidance E $\frac{\partial\Phi}{\partial A_{r}}$

W/m² Radiance L$\frac{\partial\Phi}{{\partial\left( {{A_{s} \cdot \cos}\quad \theta} \right)} \cdot {\partial\Omega}}$

W/m²/sr

With respect to Table 1, ∂A_(r) and ∂A_(s) refer to differentialelements of area on the receiver and source, respectively. Additionally,θ refers to the angle between the line of sight from the observer to thesource and the direction of the radiation 24. The associated spectralquantities are defined by differentiating the above general radiometricquantities with respect to wavelength, as depicted below:${{M_{\lambda} \equiv \frac{\partial M}{\partial\lambda}},\quad {E_{\lambda} \equiv \frac{\partial E}{\partial\lambda}},\quad {{{and}\quad L_{\lambda}} \equiv \frac{\partial L}{\partial\lambda}}}\quad$

FIGS. 4a-c are plots of the incidance of emitted radiation 24 from theelliptical reflector 12 in FIG. 2. These plots have been generated usingTracePro V2.1, a ray trace program simulating the spatial distributionof emitted radiation from the radiation source lamp 14. Morespecifically, the plots of incidance are representative of the spatialdistribution of emitted radiation at the focus of the ellipticalreflector 26 diagramed in FIG. 2.

FIG. 4a shows a plot of incidance of emitted radiation 24 from aradiation source lamp 14. The resulting incidance plot is characterizedby a substantial degree of spatial inhomogeneity. Spatial distributionof emitted radiation in particular areas of the plot is demonstrated tovary substantially throughout the incidance plot. In certain areaswithin the plot, the spatial distribution is greater than other areaswithin the same plot. The converse is also true. The spatialdistribution of the emitted radiation is also illustrated to followcertain arc-like bands of greater or lesser incidance throughout theplot.

FIG. 4b shows a plot of incidance of the same radiation source lamp ofFIG. 4a, but after a 90-degree rotation of the filament producing theincidance plot. Comparisons of the plots of FIGS. 4a and 4 b show thatareas of greater incidance in FIG. 4a are now areas of lesser incidancein FIG. 4b, and the inverse. FIG. 4c further depicts this spatialdistribution disparity by showing the changes in spatial distributionwhen the filament 16 of the same radiation source lamp 14 of FIG. 4aundergoes a vertical translation of one millimeter. Again, the spatialdistributions in FIG. 4c after the one-millimeter translation provideareas of greater incidance where there were originally none in FIG. 4a.These plots document that the spatial distribution of light at the focusof the standard light source is highly unstable with modest translationsand/or rotations of the filament.

Similar to FIGS. 4(a-c), FIGS. 5(a-c) depict plots of the intensity ofemitted radiation from the elliptical reflector in FIG. 2. These plotshave also been generated using TracePro V2.1 to simulate the angulardistribution of emitted radiation 24 from a radiation source emitter 14known in the art. More specifically, the plots are representative of theangular distribution of emitted radiation at the focus of the ellipticalreflector 26 diagramed in FIG. 2, i.e., the direction of the light raysat the focus of the elliptical reflector.

FIG. 5a shows a plot of intensity of emitted radiation from a radiationsource lamp 14. The resulting intensity plot from the standard radiationsource is characterized by a substantial degree of angularinhomogeneity. Angular distributions in particular areas of the plotalso vary dramatically within the same plot. For example, FIG. 5aillustrates a “hole” in the center of the intensity plot. The lack ofirradiation intensity in this particular area is a result of a shadowingeffect by the envelope nipple 20 on the end of a radiation source lamp14.

Rotating the filament 16 of FIG. 5a produces an intensity plotillustrated by FIG. 5b. Because the filament 16 was rotated, the hole 60in the center of the plot remains centered within the plot after the90-degree rotation. Translation of the filament 16 of FIG. 5a by onemillimeter, however, greatly diminishes the angular distribution withinthe spectroscopic system, as depicted in FIG. 5c. Angular distributionsare sporadic, and often completely shadowed by the modest translation ofthe radiation source lamp 14.

The ray trace plots of FIGS. 4(a-c) and FIGS. 5(a-c) illustrate that thespatial and angular distribution of light at the focus 26 of a standardradiation source 14 is highly unstable with respect to modesttranslations and/or rotations of its filament 16. Areas of higherincidance and intensity may form “hot spots” during illumination. In anattempt to maximize the signal-to-noise ratio, the radiation source 14could be increased to the thermal and/or comfort limits established bythe patient. However, if there are “hot spots” across the tissue, theseareas may require a lower overall radiation output and correspondingresult of lower SNR. Thus, uniform intensity illumination is desiredwhen attempting to maximize the SNR for glucose measurements.

The above plots clearly illustrate angular and spatial variancesassociated with the illumination system. These variances translate intospectroscopic variances that adversely influence the achievement of highlevels of accuracy in measuring analyte concentrations. Inhomogeneousspatial and angular distributions of emitted radiation 24 impede apractitioner from constructing chemometric models that are sensitive tothe differences between interferents and the desired analyte. Modest andunaccounted for translations and/or rotations of the emitter 14, such asthose that might result from loose mechanical tolerances or vibration,have been found to significantly alter these relied-upon chemometricmodels. An additional experiment was conducted to illustrate the effectof interferent variations on a calibrated chemometric model.

FIG. 6 shows a diagramed view of the system used for constructing achemometric model for measuring glucose concentration in the forearm'sof various subjects. The components within this instrument systemclosely resemble those in FIG. 3 and like elements are numbered thesame. The additions utilized should not be construed as an exhaustivelist for constructing an accurate chemometric model for glucosemeasurement. Identification of these additions is merely forillustrative purposes only, as one of skill in the art may readilyidentify numerous combinations of instrument components that couldachieve a chemometric model for the desired analyte.

The first of the additions shown in FIG. 6 is a five (5) millimeteraperture 70 positioned at the focal point of the elliptical reflector26. This aperture 70 limits the amount of emitted radiation 24 permittedto pass through the system 10 for analysis. Once the radiation clearsthe aperture 70, a silicon lens 72 redirects the radiation through acyan filter 76, which in turn, sends the radiation through a secondsilicon lens 74. Radiation transmitted through this series of lenses isthen filtered to absorb radiation at wavelengths at or greater than 2.7micron by passing through filter/diffuser 78. In a preferred embodiment,a WG295 filter/diffuser is utilized to absorb the wavelengths at orgreater than 2.7 micron. The radiation is then illuminated upon a sample40, collected and analyzed as described in relation to FIG. 3.

Using the above-described system, numerous calibration spectra spanninga wavelength range of approximately 1.25 μm to 2.5 μm were used toconstruct a chemometric model for measuring glucose concentrationswithin forearms' of subjects. The calibration set spanned severaldifferent lamps, many human subjects, a wide range of glucose values,and a variety of operating temperatures and relative humidities.

During the “prediction” phase of the experiment, eleven human subjectswere measured by the spectrometer system four times each day.Additionally, the radiation source lamp 14 for the system was changedevery two days. As a note, the human subjects and lamps used in thisprediction phase of the experiment were not the same as those usedduring the calibration phase. The results of this experiment are shownin FIG. 7, where the errors are sorted by day.

FIG. 7 shows a “box and whisker” plot. In this type of plot, the medianprediction error for each day is plotted as a horizontal line 82 in themiddle of a box 80, which encompasses the middle half of the data, and“whiskers” 84 are plotted at the 5th and 95th percentiles; a “dot” 86represents the mean prediction error for the day; the horizontal dashedline 88 shows where the data are centered when the prediction error biasis zero; and the numbers shown under at the bottom of the graph indicatethe number of predictions associated with that whisker and taken on eachstudy day.

FIG. 7 specifically shows a box and whisker plot of prediction errorversus day across five lamp changes, six lamps in total, over twelvedays. During the first four days of the experiment, regarding lamps 1and 2, the absolute prediction error bias was less than 20 mg/dl. Afterthe second lamp change, however, (on days 5 and 6 of the experiment) theabsolute bias increased dramatically. Replacing the third lamp with afourth (on day 7 of the experiment) reduced the bias to well under 20mg/dl.

These results suggest that the chemometric model was sufficiently“robust” as to permit accurate determination of the glucose levels forthe subjects for most of the lamps, even though the lamps used duringthe prediction phase were not the same as those used during calibration.With regard to the third lamp, however, the chemometric model failed toproduce accurate predictions. This failure suggests that the emissioncharacteristics of this lamp were substantially out of the calibrationrange used to build this experimental chemometric model.

To help isolate the emitter variation within the illumination subsystem,as the source of the prediction errors described above, anotherexperiment was conducted using the same apparatus, and similar methodsas described in the previous experiment. In this subsequent experiment,however, spectra were collected from three different subjects all on thesame day, using the same lamp throughout the prediction period. The lampwas installed in the apparatus at some arbitrary azimuthal orientation,θ₀, and spectra of the subject's forearms were taken at θ₀, as well asat θ₀ +/−2 degrees. The resulting prediction errors are plotted in FIG.8 for the three lamp orientation states. These results indicate thatchanges in the emitter characteristics, which are the result of smallrotations of the lamp, can cause prediction errors that are almost aslarge as those caused by complete replacement of one lamp with another.

A third experiment was then conducted to evaluate the effects of lampchanges on prediction error. The system utilized is depicted in FIG. 9,with like elements numbered the same as in FIG. 3 and FIG. 6. In thisexperiment, the sample source of living tissue 40 (a subject's forearm)was replaced with a “tissue phantom” 43. A tissue phantom is solid,liquid, gel, jelly or combination thereof that approximates theabsorbance and water pathlength distribution of living tissue withoutnecessarily replicating the compositional and structural properties ofliving tissue. Tissue phantoms 43 consist of a scattering solution madeof microscopic polystyrene beads suspended in water at varyingconcentrations. In this experiment, the concentration range for thepolystyrene beads was between 5000-8000 mg/dl. Tissue phantoms 43 withinthese ranges are representative substitutes for living tissue becausetheir optical scattering and absorption properties are similar to thoseof biological tissue. Additionally, the use of tissue phantoms of knownconcentrations eliminates the confounding effects often observed fromphysiological changes in living tissue. FIG. 9 diagrams the replacementof a subject's forearm 40 with a tissue phantom 41. Further, the cyanfilter 76 is located after the output fiber optic 32. In all otherrespects, the apparatus diagramed in FIG. 9 is consistent with thosediscussed in detail with respect to FIG. 3 and FIG. 6.

A set of 98 different tissue phantoms composed of 5 different analytesat different concentrations were optically sampled. In order to assessthe ability of this system in FIG. 9 to predict glucose concentrationsin the absence of lamp changes, a “cross-validation” analysis wasperformed. To accomplish this cross-validation analysis, a series ofbaseline measurements were performed wherein spectra of all ninety-eightsolutions were taken using a single lamp with the apparatus depicted inFIG. 9. This data was artificially subdivided into four sets. Usingthree of these sets, a chemometric model was constructed to predictglucose values for the remaining set. The analysis procedure was againrepeated, rotating the data sets used for calibration and prediction,until all four sets had been used for prediction. The results of thecross-validation are shown in FIG. 10a. The prediction errors biasesshown in FIG. 10a are clustered near 0 mg/dl. Such clustering suggeststhat in the absence of a lamp change, this apparatus is capable ofmaking satisfactory measurements of glucose concentration with thesesamples.

Another cross-validation analysis was then performed. In thiscross-validation analysis, the ninety-eight solutions discussed abovewere grouped into four subsets, and a different lamp was assigned foruse as the illumination source for each subset. In this analysis, datafrom three of the lamps was used to build a chemometric model to predictglucose in data from the fourth lamp. This chemometric modelingprocedure was repeated until each of the four data sets was used forprediction. The prediction results for the four data sets are presentedin FIG. 10b. A comparison between the four data sets shows a very largelamp-to-lamp prediction bias. These results are again consistent withthe findings presented in FIG. 7 (the replacement of individual lamps)and FIG. 8 (the modest rotation and/or translation of a single lamp by+/−2 degrees), thus further illustrating the deleterious effects ofinterferents, such as emitter variations, on the development of accuratechemometric models for a preferred illumination subsystem 100 of thepresent invention.

The illumination subsystem 100 of the present invention overcomes theabove-identified problems. FIG. 11 schematically depicts a simplifiedsystem incorporating means for optimizing the illumination subsystem 100to help achieve clinically relevant analytical results. In mostrespects, the apparatus diagramed in FIG. 11 is consistent with thosefeatures discussed in detail with respect to FIG. 6, with the clearidentification of a radiation homogenizer 90. In a preferred embodiment,the homogenizer 90 is positioned between the filter 78 and the sample40, as depicted in FIG. 11. At this location, entering nearlymonochromatic radiation is spatially and angularly homogenized prior toits distribution upon the sample 40.

The placement of the homogenizer 90 at the above-described location isnot to be construed as restricting the scope of the invention. Thesystem depicted in FIG. 11 is significantly simplified for illustrativepurposes. Only certain specific elements within a far more elaboratespectroscopic system are diagramed. All the elements depicted in FIG.11, however, are common to preferred spectroscopic systems of thepresent invention. The elements diagramed, therefore, are to aid inidentification of various aspects of the overall spectroscopic system.Thus, it should be understood that the present invention encompassesembodiments wherein various components of a spectroscopic system may beassembled in a relative order other than the one explicitly diagramed inFIGS. 11 and 16. However, the homogenizer 90 is placed at a pointbetween the emitter 14 and the tissue or sample 40, although otherelements may be included between the homogenizer 90 and emitter 14 orbetween the homogenizer 90 and tissue or sample 40. This can alsoinclude the spectrometer 44, which in certain embodiments can bepositioned between the emitter 14 and tissue 40.

In a preferred embodiment, the radiation homogenizer 90 is a light pipe.FIGS. 12a and 12 b show a perspective end view and a detail plan view ofa light pipe 91 of the present invention. Light pipe 91 is generallyfabricated from a metallic, glass (amorphous), crystalline, polymeric,or other similar material, or any combination thereof. Physically, thelight pipe comprises a proximal end 92, a distal end 94, and a length 96therebetween. The length of a light pipe 91, for this application, ismeasured by drawing a straight line from the proximal end 92 to thedistal end 94 of the light pipe. Thus, the same segment of light pipe 91may have varying lengths depending upon the shape the segment forms. Thelength of the segment readily varies with the light pipe's intendedapplication.

In a preferred embodiment as illustrated in FIGS. 12a and 12 b, thesegment forms an S-shaped light pipe. The S-shaped bend in the lightpipe provides angular homogenization of the light as it passes throughthe light pipe. This conclusion is documented by the experiment anddiscussion associated with FIGS. 14a-c and 15 a-c below. It is, however,recognized that angular homogenization can be achieved in other ways. Aplurality of bends or a non-S-shaped bend could be used. Further, astraight light pipe could be used provided the interior surface of thelight pipe included a diffusely reflective coating over at least aportion of the length. The coating provides angular homogenization asthe light travels through the pipe. Alternatively, the interior surfaceof the light pipe can be modified to include dimples or“microstructures” such as micro-optical diffusers or lenses toaccomplish angular homogenization. Finally, a ground glass diffusercould be used to provide some angular homogenization.

The cross-section of the light pipe 91 may also form various shapes. Inparticular, the cross-section of the light pipe 91 is preferablypolygonal in shape to provide spatial homogenization. Polygonalcross-sections include all polygonal forms having three to many sides.Certain polygonal cross-sections are proven to improve spatialhomogenization of channeled radiation. For example, a light pipepossessing a hexagonal cross-section the entire length thereof providedimproved spatial homogenization when compared to a light pipe with acylindrical cross-section of the same length.

Additionally, cross-sections throughout the length of the light pipe mayvary. As such, the shape and diameter of any cross-section at one pointalong the length of the light pipe may vary with a second cross-sectiontaken at a second point along the same segment of pipe.

In certain embodiments, the light pipe is of a hollow constructionbetween the two ends. In these embodiments, at least one lumen may runthe length of the light pipe. The lumens of hollow light pipes generallypossess a reflective characteristic. This reflective characteristic aidsin channeling radiation through the length of the light pipe so that theradiation may be emitted at the pipe's distal end. The inner diameter ofthe lumen may further possess either a smooth, a diffuse or a texturedsurface. The surface characteristics of the reflective lumen aid inspatially and angularly homogenizing radiation as it passes through thelength of the light pipe.

In additional embodiments, the light pipe is of solid construction. Thesolid core could be cover-plated, coated or clad. Again, a solidconstruction light pipe generally provides for internal reflection. Thisinternal reflection allows radiation entering the proximal end of thesolid light pipe to be channeled through the length of the pipe. Thechanneled radiation may then be emitted out of the distal end of thepipe without significant loss of radiation intensity. An illustration ofinternal reflection and the resulting channeling is shown in FIG. 13.

FIG. 13 depicts a plan view of a ray trace showing radiation 24 from alight source 14 (40-watt tungsten-halogen bulb) focused by an ellipticalreflector 12 into, and through, a light pipe 91 of the presentinvention. In particular, FIG. 13 illustrates how emitted radiation froma radiation source lamp is focused upon the proximal end of the lightpipe of the present invention. The focused radiation is internallyreflected throughout the length of the light pipe. As the radiation isreflected, specific structural characteristics of the light pipe (herean S-shaped segment of hexagonal cross-sectioned pipe) angularly andspatially homogenizes the resulting radiation emitted at the pipe'sdistal end.

FIGS. 14(a-c) are plots of the incidance of emitted radiation from theelliptical reflector and light pipe depicted in FIG. 13. These plotshave again been generated using TracePro V2.1, a ray trace programsimulating the spatial distribution of emitted radiation from theradiation source emitter. More specifically, the plots of incidance arerepresentative of the spatial distribution of emitted radiation at thedistal end of the light pipe diagramed in FIG. 13.

FIG. 14a shows a plot of incidance of emitted radiation from theradiation source lamp coupled to the light pipe of the presentinvention. The resulting incidance plot is characterized by asubstantial degree of spatial homogeneity. Spatial distribution ofemitted radiation throughout the incidance plot varies slightly. Acomparison of FIG. 14a with that of FIG. 4a illustrates the substantialimprovement in spatial distribution throughout the incidance plot whenusing a light pipe of the present invention.

FIG. 14b shows a plot of incidance of the same radiation source lampcoupled to the light pipe of the present invention as depicted in FIG.14a, but after a 90-degree rotation of the filament producing theincidance plot. Again, the resulting incidance plot is characterized bya substantial degree of spatial homogeneity. In fact, there exist fewdetectable difference in spatial distribution after the resulting90-degree rotation as with the spatial distribution prior to therotation.

FIG. 14c further depicts the spatial homogeneous distribution of emittedradiation using a light pipe of the present invention. Again, thespatial distribution in FIG. 14c, after a one-millimeter translation, isvery similar to those spatial distributions in FIGS. 14(a-b).

Similar to FIGS. 14(a-c), FIGS. 15(a-c) show plots of the intensity ofemitted radiation from the elliptical reflector and light pipe depictedin FIG. 13. These plots have also been generated using TracePro V2.1 tosimulate the angular distribution of emitted radiation from a radiationsource emitter known in the art. More specifically, the plots ofintensity are representative of the angular distribution of emittedradiation at the distal end of the light pipe diagramed in FIG. 13.

FIG. 15a shows a plot of intensity of emitted radiation from theradiation source lamp coupled to the light pipe of the presentinvention. The resulting intensity plot from the standard radiationsource is characterized by a substantial degree of angular homogeneity.Angular distributions throughout the plot vary slightly. A comparison ofFIG. 15a with that of FIG. 5a illustrates the substantial improvement inangular distribution throughout the intensity plot when using a lightpipe of the present invention. For example, the “hole” in the center ofthe intensity plot caused by the glass nipple on the end of theradiation source lamp is no longer present and is now replaced withhomogenized angular radiation.

Rotating the filament of FIG. 15a by 90-degrees produces an intensityplot illustrated by FIG. 15b. Again, there are minor differences betweenthe intensity plots after, and prior to, the rotation. Translation ofthe filament of FIG. 15a by one millimeter, as depicted in FIG. 15c,once again documents reduction in variation of angular distribution ascompared to the plots of FIGS. 15a-b.

The ray trace plots of FIGS. 14(a-c) and 15(a-c) illustrate that thespatial and angular distribution of light at the output of the lightpipe is highly stable with respect to modest translations and/orrotations of its filament. This is especially clear when comparing theray trace plots of FIGS. 14(a-c) and FIGS. 15(a-c) using a light pipe ofthe present invention with FIGS. 4(a-c) and FIGS. 5(a-c) without thelight pipe of the present invention. The light tube of the presentinvention has been effectively shown through these incidance andintensity plots to eliminate or substantially reduce the light source orillumination system as an interferent associated with chemometricmodeling. It has been found that the use of the light pipe in theillumination subsystem of the present invention allows construction ofchemometric models of sufficient sensitivity to measure analyteconcentrations.

Another embodiment of the illumination subsystem of the presentinvention is depicted schematically in FIG. 16. In this embodiment, thetungsten halogen source 14 is placed at one focus of an ellipticalreflector 110 and the proximal end 111 of a light pipe 112 is placed atthe other focus 114. To improve the collection efficiency of the systema separate back reflector 116 is positioned opposite the ellipticalreflector 110 to capture and redirect light which would otherwise belost from the system. The distal end 118 of the light pipe 112 thenprovides the source of radiation for the spectroscopic sample.

FIGS. 17 and 18 show the simulated spatial and angular distributions ofthe light at the distal end 118 of the light pipe 112 of FIG. 16. Thesedistributions show substantially improved homogenization as compared tothe output of the standard system depicted in FIG. 2.

Another embodiment of the present invention is shown in FIG. 19. In thisembodiment, the tungsten halogen source 114 is placed at the focus 120of a section of a parabolic reflector 122 and the proximal end 124 of alight pipe 126 is placed at the focus 128 of a section of anotherparabolic reflector 130. The homogenized light exits the distal end 132of the light pipe 126. The simulated spatial and angular distributionsof the light at the distal end of the light pipe, shown in FIGS. 20 and21, show substantially improved homogenization as compared to the outputof the standard system depicted in FIG. 2.

Another embodiment of the present invention is shown in FIG. 22. Thisembodiment is similar to the standard system depicted in FIG. 2, exceptthat the standard elliptical reflector has been replaced with a facetedreflector 140. This faceted reflector 140 has the same general form asthe elliptical reflector of FIG. 2, but the smoothly varying shape ofthe standard elliptical form has been replaced with flat mirror facets142 which locally approximate the standard shape. Such facetedreflectors 142 provide a high degree of spatial uniformity. FIG. 23 is asimulated spatial distribution of the light at the second focus of theellipse, showing substantially improved spatial homogeneity as comparedto the output of the standard system of FIG. 2. FIG. 24 is a simulatedangular distribution at the second focus of the ellipse which, unlikethe other embodiments disclosed herein, exhibits a high degree ofnon-uniformity.

The faceted elliptical reflector is an example of an embodiment of anillumination subsystem of the present invention which produces only partof the desired characteristics in the output radiation. In the case ofthe faceted reflector 140, spatial homogenization is achieved but notangular homogenization. In other cases, such as passing the output ofthe standard system through ground glass, angular homogenization isachieved but not spatial homogenization. In embodiments such as these,where only angular or spatial homogenization is produced (but not both)some improvement in the performance of the spectroscopic system may beexpected. However, the degree of improvement would not be expected to beas great as for systems where spatial and angular homogenization of theradiation are simultaneously achieved.

Another method for creating both angular and spatial homogenization isto use an integrating sphere in the illumination subsystem. Althoughcommon to use an integrating sphere for detection of light, especiallyfrom samples that scatter light, integrating spheres have not been usedas part of the illumination subsystem when seeking to measure analytesnon-invasively. In practice, radiation output from the emitter could becoupled into the integrating sphere with subsequent illumination of thetissue through an exit port. The emitter could also be located in theintegrating sphere. An integrating sphere will result in exceptionalangular and spatial homogenization but the efficiency of this system issignificantly less than other embodiments previously specified.

In order to evaluate the efficacy of the light tube of the presentinvention for reducing prediction error related to lamp variations, anexperiment was conducted comparing a chemometric model using a lightpipe of the present invention with a chemometric model without the lightpipe of the present invention. The system of FIG. 9 depicts the systemwithout the light pipe. FIG. 25 is a diagramed view of the system of thepresent invention for measuring glucose in scattering media having atissue phantom 43 as the sample source. The apparatus diagramed in FIG.25 is consistent with that discussed in detail with respect to FIG. 9except for the S-bend light pipe 91 which is included at the focus ofthe second silicon lens 74.

The results of comparative testing between the system of FIG. 9 and thatof FIG. 25 which incorporates the light pipe are included in the box andwhisker plots of FIGS. 26a through 26 d. FIGS. 26a and 26 b areduplicates of FIGS. 10a and 10 b to provide easy comparison with theresults included in FIGS. 26c and 26 d. Thus, FIG. 26a depicts theability of the standard system with no bulb changes to predict glucoseconcentrations. FIG. 26b depicts the system ability across four bulbchanges. FIG. 26c depicts the results of the system of FIG. 25 acrossfour bulb changes. FIG. 26d shows the results of tests done on thesystem of FIG. 25, but with the addition of a ground glass diffuser 78prior to the light pipe 91. FIGS. 26c and 26 d clearly show that theembodiments of FIG. 25 are highly effective in improving the predictiveaccuracy of the apparatus and chemometric model over the system of FIG.9. Further, the greatest benefit is derived when the ground glassdiffuser 78 and the S-bend light pipe 91 are used together which resultsin the highest degree of homogenization of the light incident on thesample.

The performance of the illumination subsystem of the present inventionrelative to a known radiation emitter difference can be quantified. Amethod for quantifying the performance of the illumination system is tocreate both angular and spatial distribution plots under two known butdifferent conditions. The differences between the two similar metricplots can be quantified. The known emitter difference to be used forquantification is preferably a one-millimeter translation of the lampfilament.

Angular and spatial distribution plots can be created by using standardray trace packages such as TracePro V2.1 or through direct measurements.The image of the illumination system beam can be measured by using anystandard intensity mapping scheme and by using a goniometer. This allowsboth the spatial and angular distributions of the illumination output tobe determined.

Optical modeling or direct measurement of the system should occur beforeand after movement of the filament. In order to standardize thecalculation for many applications, the image should be divided intoapproximately one hundred equally sized “bins” (or squares), with tenbins across the diameter of the output image. This requirement is easilysatisfied when performing ray trace analysis and can be accomplished byeither measuring the beam in a ten by ten grid or by sampling at finerspacing and then averaging the data. The spatial and angulardistributions for the initial emitter state are then subtracted from thecorresponding distributions after movement of the lamp filament by onemillimeter. The resulting images represent either the angular or spatialvariance that occurred due to the emitter perturbation. In order toquantify the angular or spatial variance, all the data in the differentimages are put into a vector for easier calculation, and the vector isnormalized so that its length equals 1. This normalization is achievedby dividing each data point by the 2-norm (∥.∥₂), which is equivalent tothe Euclidean distance of the vector, $\begin{matrix}{{x}_{2} = \left( {\sum\limits_{i = 1}^{n}\quad {x_{i}}^{2}} \right)^{1/2}} & {{Eq}.\quad (1)}\end{matrix}$

where X is the vector of the difference image and n is the number ofdata points in the vector.

The normalization step ensures that the magnitude of everydifference-image is comparable. Following the normalization step, thestandard deviation of the normalized image vector is calculated, andthis metric is an indication of the amount of variance introduced by theknown emitter difference, $\begin{matrix}{{Metric} = \frac{\sum\limits_{i = 1}^{n}\left( {\frac{x_{i}}{{x}_{2}} - {{mean}\left( \frac{x_{i}}{{x}_{2}} \right)}} \right)^{2}}{n - 1}} & {{Eq}.\quad (2)}\end{matrix}$

The standard deviation of the normalized image vector for both angularand spatial distributions was calculated for three differentillumination systems.

1. Acceptable System: This illumination system is a light source(40-watt tungsten-halogen bulb) focused by an elliptical reflector intoa ground glass diffuser, specified as a weak angular homogenizer, withsubsequent coupling into a hexagonal light pipe with a length todiameter aspect ratio of 3 to 1. The system is modeled such that thefilament image fully fills the input into the hexagonal light pipe.

2. Preferred System: the illumination system is the same as theacceptable except that the length to diameter aspect ratio is 7 to 1.

3. Ideal System: The illumination system is composed of a light source(40-watt tungsten-halogen bulb) focused by an elliptical reflector intoa ground glass diffuser, specified as a strong angular homogenizer, withsubsequent coupling into an s-bend hexagonal light pipe with a length todiameter aspect ratio of 7 to 1. The system is modeled such that thefilament image fully fills the input into the hexagonal light pipe.

Based upon testing with these three illumination systems, the degree ofhomogenization can be generally classified as acceptable, preferred andideal. Table 2 shows the standard deviations of the spatial distributionfor the three systems. Table 3 shows the standard deviation for angulardistribution.

TABLE 2 Vertical Filament Filament Rotation Acceptable 0.053 0.050Preferred 0.045 0.042 Ideal 0.039 0.034

TABLE 3 Vertical Filament Filament Rotation Acceptable 0.044 0.066Preferred 0.032 0.054 Ideal 0.027 0.050

There is another metric that can be used to evaluate the efficacy of anillumination system in reducing error inflation following bulb changes.This metric is known as the multivariate signal to noise (mSNR). Thetypical signal to noise (S/N) calculation is a univariate measure; it isdefined as the maximum signal in a spectrum divided by the standarddeviation of the baseline noise.

When a multivariate calibration is used, the signal from two or morewavelengths is used to quantify the analyte of interest. Because ofthis, unless the noise is random or ‘white’ noise, the standarddeviation of the baseline (as used in univariate S/N calculations) is aninexact and inappropriate noise estimate. Furthermore, the maximumsignal in the spectrum is also an inexact and inappropriate measure ofthe overall signal since the multivariate calibration uses signals frommultiple wavelengths. The mSNR metric, however, uses the multivariatenet analyte signal and the error covariance matrix and therefore gives abetter estimate of the signal to noise for multivariate calibrations.

The net analyte signal is that part of the analyte spectrum which isorthogonal (contravariant) to the spectra of all interferents in thesample. If there are no interfering species, the net analyte spectrum isequal to the analyte spectrum. If interfering species with similarspectra to the analyte are present, the net analyte signal will bereduced relative to the entire spectrum. The concept of net analytesignal for a three-component system is depicted graphically in FIG. 57.Because the calibration depends on the net analyte signal, themultivariate signal to noise metric takes this measure into account.

The mSNR can be calculated if two pieces of information are known. Thenet analyte signal (NAS) for the analyte of interest must be known, butthis may be estimated from the regression vector, b (the model),$\begin{matrix}{{NAS} = \frac{\hat{b}}{{\hat{b}}_{2}^{2}}} & {{Eq}.\quad (3)}\end{matrix}$

where ∥.∥₂ represents the 2-norm of the vector.

The error covariance matrix (Σ), which describes the error structure ofthe multi-wavelength spectral data, is also needed for the mSNRcalculation,

Σ=ε^(T)*ε  Eq. (4)

where ε is a vector containing the noise at each wavelength.

x=x₀=ε  Eq. (5)

where x is a measured spectrum, x₀ is the “true” spectrum in the absenceof noise, and ε is the noise.

The error covariance matrix, Σ, measures how noise is correlated acrosswavelengths. The spectra used to calculate the error covariance matrixare spectra that have a constant amount of the analyte of interest andare obtained or processed in a manner to identify the spectral variancesdue to the variance of interest. In practice, a repeat sample should beused and the only variance introduced into the system should be thespectral variance being identified. In this invention, the variancesource of interest is spectral variance due to emitter changes. Thus,spectral data from a repeat sample is obtained using different emitters.If the noise is uncorrelated, the error covariance matrix will have nooff-diagonal elements, but in many cases, this will not be true. In suchcases, the error may ‘overlap’ spectrally with the net analyte signal.In other words, this will introduce ‘Noise’ into the measurement of thisparticular analyte. The ‘Noise’ may be calculated as,

Noise={square root over (v^(T)Σv)}  Eq. (6)

where $\begin{matrix}{v = \frac{NAS}{{{NAS}}_{2}}} & {{Eq}.\quad (7)}\end{matrix}$

The mSNR at unit concentration may then be calculated by,$\begin{matrix}{{mSNR} = {\frac{{{NAS}}_{2}}{Noise} = \frac{{{NAS}}_{2}}{\sqrt{v^{T}{\sum v}}}}} & {{Eq}.\quad (8)}\end{matrix}$

The inverse of the net analyte spectrum, 1/mSNR, is an estimate of howmuch error will be added to prediction estimates if the type of noise inε is present in the spectra being used to predict the analyteconcentration (or other property).

When an illumination system is insensitive to emitter variances, therewill be little effect on the spectral noise; in other words, the errorcovariance matrix, Σ, will be close to diagonal. In that case, the mSNRwill be high. In the case where the system is sensitive to emittervariances or source fluctuations, correlated noise will be introducedand that will create off-diagonal elements which will be present in theerror covariance matrix Σ. When these spectral variances or noiseinterfere (co-vary) with the net analyte signal, the mSNR gets smallerand its inverse increases.

Table 4 shows the mSNR and 1/mSNR values calculated for three differentillumination systems. These systems include a standard system with nobulb changes, the preferred embodiment system (with s-bend light pipeand diffuser) and also one that contained a straight light pipe(acceptable system).

TABLE 4 System mSNR 1/mSNR No bulb change (Ideal level) 0.2 5 Bent lightpipe & diffuser (Preferred level) 0.033 30 Straight light pipe only(Acceptable level) 0.0166 60

It is clear that bulb changes influence each system differently. ThemSNR is highest when no bulb change occurs, and lowest when the standardsystem with limited source homogenization is used. Conversely, thegreatest inflation in prediction errors can be seen in that system(approximated by 1/mSNR).

These mSNR values were calculated using the study measuring the98-solution set that was described previously. The NAS was calculatedusing the model (b) generated from the data set where a single bulb wasused (equation 1). This model had no knowledge of bulb changes, and sothe net analyte signal corresponds to that in the absence of sourcefluctuations. For each illumination system, there were four bulb changesas described before. For each bulb, in addition to the 90-solution set,additional ‘repeat’ samples were measured. These ‘repeats’ were simplysamples that contained all of the analytes at concentrations at thecenter of the calibration. Thus, to isolate the spectral variance due tobulb changes the spectral data was processed in the following manner.Multiple ‘repeat’ spectra at each bulb were measured, and the averagerepeat spectrum for each bulb was calculated using these data, hereafterreferred to as the average bulb spectrum. Each average bulb spectrum canbe thought of as the ‘x’ in equation 5. The mean repeat spectrum issimply the average spectrum of the average bulb spectra. To calculatethe error, ε, associated with each bulb, the mean repeat spectrum wassubtracted from the average bulb repeat spectra, $\begin{matrix}{ɛ_{i} = {x_{i} - \frac{\sum\limits_{i = 1}^{n}\quad x_{i}}{n}}} & {{Eq}.\quad (9)}\end{matrix}$

where n is the number of bulbs in the analysis (4 in this example). TheΣ matrix was then calculated using equation 4, and equations 6-8 werethen calculated to find the mSNR.

Now referring to FIG. 27, another aspect of the present invention isdepicted. The system depicted provides spectral filtering or bandpassfiltering to eliminate unnecessary wavelengths or bands of wavelengthsfrom the light prior to contact with the tissue. This is accomplished byplacing one or more elements between the light source and tissue. Theelements can include absorptive filters fabricated of any transparent orpartially transparent substrate; single layer or multi-layer dielectriccoatings deposited on any transparent or partially transparentsubstrate; a grating or prism which disperses the radiation, permittingunwanted wavelengths to be blocked from reaching the tissue; and/or anaperture which selectively blocks undesirable radiation.

A preferred system for bandpass filtering is depicted in FIG. 27 whichdepicts a light source 101 placed within an electrical reflector 102.FIG. 27 also depicts a hexagonal S-bend light pipe 104 to receive lightfrom the source 101. A series of filters are placed between the lightsource 101 and the light pipe 104. The first optical filter is a siliconfilter 106 which is anti-reflection coated to transmit at least ninetypercent (90%) of the in band incident light. The silicon filter passeswavelengths of light longer than 1.1 micron. The second optical filteris preferably a KOPP 4-67 colored glass filter 108 that, in combinationwith the silicon filter, passes light in the 1.2 to 2.5 micron spectralregion. The slope of the KOPP filter is such that is preferentiallypasses light at wavelengths between 2.0 and 2.5 micron. The thirdoptical filter is an ORIEL WG295 absorption filter 110 that cuts outwavelengths longer than 2.5 micron. The front surface of the WG295filter can be polished or finely ground. If the front surface is finelyground, the WG295 acts as a diffuser as well as a light filter. It hasbeen found that these filters prevent burning of the tissue, whileenhancing the signal-to-noise ratio of the system by band limiting thelight to only the spectral region of interest. The effect of bandlimiting the light is to reduce shot noise generated by the photon fluxincident on the detector.

An alternative combination of filters to achieve spectral bandpassfiltering is depicted in FIG. 6. With this embodiment, the two siliconlenses 72,74 absorb wavelengths shorter than approximately 1.2 micronsand longer than approximately 10 microns. The cyan filter 76 is anabsorptive filter such as a Hoya CM-500 to absorb mid-infrared radiationat wavelengths of approximately 2.8 microns and longer. Further, aSCHOTT WG-295 absorptive filter 78 is included to absorb radiation atwavelengths approximately 2.7 micron and higher. FIG. 28 graphicallydepicts the individual and combined spectral transmission of thecomponents shown in FIG. 6, along with the “spectral fingerprint” ofglucose. As depicted in the graphs, this combination of absorptivefilters and silicon lenses acts to block unwanted wavelengths, whilestill permitting transmission of radiation in the glucose fingerprintregion. Similar combinations of filters can be utilized based onanalytes of interest to be analyzed.

It is also recognized that other modifications can be made to thepresent disclosed system to accomplish desired homogenization of light.For example, the light source could be placed inside the light pipe in asealed arrangement which would eliminate the need for the reflector.Further, the light pipe could be replaced by an integrator, wherein thesource is placed within the integrator as disclosed in U.S. patentapplication Ser. No. 09/832,631, entitled “Encoded Multiplex VariableFilter Spectrometer,” filed on the same date herewith and incorporatedby reference.

The purpose of the tissue sampling subsystem 200 is to introduceradiation generated by the illumination subsystem 100 into the tissue ofthe subject and to collect the portions of the radiation that are notabsorbed by the tissue and send that radiation to the FTIR spectrometersubsystem 400 for measurement. FIGS. 29, 30 and 31 depict elements of apreferred tissue sampling subsystem 200. Referring first to FIG. 29, thetissue sampling subsystem 200 has an optical input 202, a samplingsurface 204 which forms a tissue interface 206 that interrogates thetissue and an optical output 207. The subsystem further includes anergonomic apparatus 210, depicted in FIG. 31, which holds the samplingsurface 204 and positions the tissue at the interface 206. In apreferred subsystem, a device that thermostats the tissue interface isincluded and, in some embodiments, an apparatus which repositions thetissue on the tissue interface in a repetitive fashion is included.

The optical input 202 of the tissue sampling subsystem 200 receivesradiation from the illumination subsystem 100 (i.e., light exiting thelight pipe) and transfers that radiation to the tissue interface 206.The optical input may consist of a bundle of optical fibers that arearranged in a geometric pattern that collects the most light possiblefrom the illumination subsystem. One preferred arrangement is depictedin FIG. 30. The plan view depicts the ends of the input and outputfibers in a geometry at the sampling surface including six clusters 208arranged in a circular pattern. Each cluster includes four centraloutput fibers 212 which collect diffusely reflected light from thetissue. Around each grouping of four central output fibers 212 is acylinder of material 215 which ensures about a 100 μm gap between theedges of the central output fibers 212 and the inner ring of inputfibers 214. The 100 μm gap is important to target glucose in the dermis.As shown in FIG. 30, two concentric rings of input fibers 214 arearranged around the cylinder of material 215. As shown in one preferredembodiment, 32 input fibers surround the four output fibers. The highratio of input-to-output fibers is maintained in all preferredembodiments in recognition of loss within the tissue.

All of the clustered input and output fibers are potted into a clusterferrule which is glued into a sampling head 216. The sampling head 216includes the sampling surface 204 which is polished flat to allowformation of a good tissue interface. Likewise, the input fibers areclustered into a ferrule 218 connected at the input ends to interfacewith the illumination subsystem 100. The output ends of the outputfibers are clustered into a ferrule 220 for interface with the FTIRspectrometer subsystem 400.

Alternatively, the optical input may not require any fibers and mayinstead use a combination of light pipes, refractive and/or reflectiveoptics to transfer the maximum amount of input light to the tissueinterface. It is important that the input optics of the tissue samplingsubsystem collect as much light as possible from the illuminationsubsystem 100 in order to maximize the SNR achieved by the overallsystem. In the art, FTIR spectrometer-based non-invasive glucosemonitoring systems have been described with the illumination subsystembefore the FTIR spectrometer and the tissue sampling subsystem after theFTIR spectrometer. This configuration as described in the art has thedisadvantage of limiting the total throughput of the system because theFTIR spectrometer cannot support a large range of angles from theillumination subsystem due to spectral resolution and physical sizerequirements. In the present invention, the placement of theillumination subsystem 100 and tissue sampling subsystem 200 before theFTIR spectrometer subsystem 400 results in over an order of magnitudeimprovement in throughput for a given size of FTIR spectrometer becausethe input to the tissue sampling subsystem 200 is designed to handle thewide range of angles from the illumination subsystem 100 and the smalloutput image size of the tissue sampling subsystem is better matched tothe throughput supported by a reasonably sized FTIR spectrometer. Thesource, sample, FTIR spectrometer, detector (SSFD) configuration fornon-invasive glucose monitoring is a significant improvement over thecurrent art.

The tissue interface is another critical part of the tissue samplingsubsystem. It must irradiate the tissue in a manner that targets theglucose bearing compartments of the tissue and discriminates againstlight that does not travel a significant distance through thosecompartments. As stated above, the 100-μm gap discriminates againstlight which contains little glucose information. In addition, the tissueinterface may need to average over a certain area of the tissue toreduce errors due to the heterogeneous nature of the tissue. The tissuesampling interface should reject specular and short pathlength rays andit must collect the portion of the light that travels the desiredpathlength through the tissue with high efficiency in order to maximizethe SNR of the system. The tissue sampling interface may employ opticalfibers to channel the light from the input to the tissue in apredetermined geometry as discussed above. The optical fibers may bearranged in pattern that targets certain layers of the tissue thatcontain good glucose concentration information. The spacing andplacement of the input and output fibers can be arranged in an optimalmanner to achieve effective depth targeting. In addition to the use ofoptical fibers, the tissue sampling interface can use a non-fiber basedarrangement that places a pattern of input and output areas on thesurface of the tissue when using diffuse reflectance. Proper masking ofthe non-fiber based tissue sampling interface ensures that the inputlight travels a minimum distance in the tissue and contains validglucose concentration information. Finally, the tissue samplinginterface may be thermostatted to control the temperature of the tissuein a predetermined fashion. The temperature of the tissue samplinginterface is set such that the invention reduces prediction errors dueto temperature variation and also such that glucose direction of changecan be inferred by the equilibration of the interstitial space withcapillary blood glucose levels. In preferred embodiments, the samplinghead 216 is heated to between 34° C. and 40° C. in order to thermostatthe tissue. This promotes equilibration of glucose between theinterstitial fluid and the capillary blood. Further, reference errorsare reduced when building a calibration model. These methods aredisclosed in commonly assigned U.S. patent application Ser. No.09/343,800, entitled “Method and Apparatus for Non-Invasive BloodAnalyte Measurement with Fluid Compartment Equilibration,” thedisclosure of which is incorporated herein by reference.

The tissue sampling subsystem generally will employ an ergonomicapparatus or cradle 210 that positions the tissue over the samplinginterface 206 in a reproducible manner. A preferred ergonomic apparatus210 is depicted in FIG. 31. In the case of sampling the underside of theforearm, an ergonomic cradle design is essential to ensure good contactwith the sampling interface. The ergonomic cradle 210 includes a base221 having an opening 223 therethrough. The opening is sized forreceiving the sample head 216 therein to position the sampling surface204 generally coplanar with an upper surface 225 of the base 221. Theergonomic cradle 210 references the elbow and upper arm of the subjectvia a bracket 222 in conjunction with a float-to-fit handgrip 224 toaccurately position the forearm on the tissue sampling interface.Careful attention must be given to the ergonomics of the tissue samplinginterface or significant sampling error can result. Errors in samplingthe tissue have been found to be a major source of reduced accuracy andprecision for the non-invasive measurement of glucose.

The ergonomic cradle 210 of the present invention is an important partof the tissue sampling subsystem 200. The cradle is designed such thatthe forearm of the subject is reliably located over the sample head 216.The bracket 222 forms an elbow rest that sets the proper angle betweenthe upper arm and the sampling head 216, and also serves as aregistration point for the arm. The adjustable hand rest 224 is designedto hold the fingers in a relaxed manner. The hand rest position isadjusted for each subject to accommodate different forearm lengths. Inpreferred embodiments, a lifting mechanism is included which raises andlowers the cradle periodically during sampling to break and reform thetissue interface. Reformation of the interface facilitates reduction ofsampling errors due to the rough nature and inhomogeneity of the skin.

The image formed by the output of the tissue sampling subsystem istypically an order of magnitude smaller in size than its input. Thisinput image to output image ratio is necessary to match the throughputsupported by the FTIR spectrometer while maximizing the overall systemsignal to noise ratio. The output of the tissue sampling subsystem 200transfers the portion of the light not absorbed by the tissue that hastraveled an acceptable path through the tissue to the input of the FTIRspectrometer subsystem 400. The output of the tissue sampling subsystem200 may use any combination of refractive and/or reflective optics toproduce a collimate beam that will be modulated by the FTIRspectrometer. In preferred embodiments, the diffusely reflected lightcollected by the output fibers 207 of the sampler head 216 arecollimated by a plano-aspheric lens made of ZnSe. The design of the lensis such that the collimated beam has less than five degrees ofdivergence. This lens 228 is schematically depicted in FIG. 1 as part ofthe FTIR spectrometer subsystem 400. The collimating lens 228 produces abeam with low optical distortion that serves as the proper input to theFTIR spectrometer discussed below.

As shown in FIG. 1, the FTIR spectrometer subsystem 400 includes aspectrometer 230 that modulates the sufficiently collimated light fromthe tissue sampling subsystem 200 to create an interferogram which isreceived by a detector 232. The interferogram spatially encodes the NIRspectrum collected by the tissue sampling subsystem. FIG. 32schematically depicts one embodiment of an FTIR spectrometer 230 whichincludes a beamsplitter 234 and compensator optics 236, a fixedretro-reflector 238 and a moving retro-reflector 240. The collimatedinput light 242 impinges on the beamsplitter optic 234 and is partiallyreflected and partially transmitted by the coating on the back surfaceof the beamsplitter 234. The reflected light passes back through thebeamsplitter optic 234 and reflects off the fixed retro-reflector 238and back to the beamsplitter 234. The transmitted light passes throughthe compensator optic 236 and reflects off the moving retro-reflector240 and back to the beamsplitter 234. The transmitted and reflectedportions of the light recombine at the beamsplitter to create aninterference pattern or interferogram. The amount of constructive and/ordestructive interference between the transmitted and reflected beams isdependent on the spectral content of the collimated input beam 242 andon the optical path difference between the fixed retro-reflector 238 andthe moving retro-reflector 240.

FIG. 33 shows a typical interferogram created by an FTIR spectrometer.At the point of zero path difference between the transmitted andreflected beams, there will be maximum constructive interference, andthe centerburst of the interferogram is created. The interferogram isthen focused onto a detector 232, as shown in FIG. 1. The detector 232converts the optical interferogram into an electrical representation ofthe interferogram for subsequent digitizing by the data acquisitionsubsystem 500.

In a preferred embodiment, the non-invasive glucose monitor FTIRspectrometer subsystem 400 utilizes an FTIR spectrometer 230manufactured by Bomem. This spectrometer utilizes a single plate thatcontains beamsplitter and compensator functions. In addition, cubecorners are used as the end mirrors and both cube corners are moved on awishbone suspension to create the optical path difference and thesubsequent interference record. The Bomem WorkIR™ FTIR spectrometerachieves the desired thermal stability and spectral complexityperformance necessary for making non-invasive glucose measurements withNIR spectroscopy. The FTIR spectrometer modulates the collimated lightfrom the tissue sampler to spatially encode the NIR spectrum into aninterferogram. The spectral resolution of the interferogram can be inthe range of 7.5 to 64 wavenumbers. The preferred range of spectralresolution is 30-50 wavenumbers. The interferometer will produce eithera single-sided or a double-sided interferogram, with the double-sidedinterferogram being preferred because it achieves a higher SNR. Theresulting interferogram is preferably passed to a condensing lens 244,as shown in FIG. 1, and this lens focuses the light onto the detector232. The condensing lens 244 is a double convex design with each surfacebeing aspherical in nature. The lens material is ZnSe. The detector 232is preferably a thermo-electrically cooled, 1 mm diameter, extendedrange, InGaAs detector that is sensitive to light in the 1.2 to 2.5 μmregion of the spectrum. The detector 232 converts the opticalinterferogram into its electrical equivalent.

The non-invasive measurement of glucose in humans places extremerequirements on the performance of the instrumentation due to the verysmall size of the glucose absorption spectrum relative to the waterabsorption of the body. In addition, interferences due to absorption ofother spectroscopically active compounds such as collagen, lipid,protein, etc. reduce the useful portions of the glucose absorptionspectrum, yielding a net analyte signal that is very small. To firstorder approximation, 1 mg/dl of glucose concentration change isequivalent to 1 μAu of spectral variance for the effective pathlengthlight travels through tissue using the present invention. Therefore, inorder to measure glucose non-invasively with clinically acceptableaccuracy, the spectrometer portion of the non-invasive glucose monitormust have a very large signal-to-noise ratio (SNR) and must haveexcellent photometric accuracy.

The FTIR spectrometer is a critical component of the non-invasivemeasurement glucose monitoring system of the present invention becauseit can achieve the required high SNR and photometric accuracy. In theart, there are hundreds of variants of the classic Michelsoninterferometer design depicted in FIG. 32. One preferred interferometerdesign is disclosed in commonly assigned U.S. patent application Ser.No. 09/415,600, filed Oct. 8, 1999, entitled “InterferometerSpectrometer with Reduced Alignment Sensitivity,” the disclosure ofwhich is incorporated herein by reference. The FTIR spectrometer hasthroughput advantages (Jaquinot and Fellget advantages) relative todispersive spectrometers and acousto-optical tunable filters. Inaddition to high throughput, the use of a reference laser in the FTIRspectrometer gives the device wavenumber axis precision. Wavenumber orwavelength axis precision is very important for effective calibrationmaintenance and calibration transfer.

The FTIR spectrometer subsystem must achieve certain minimum performancespecifications for thermal stability, spectral complexity and modulationefficiency. In real world use of the present invention, ambienttemperature and relative humidity will vary with time. Over an ambienttemperature operating range of 10 C. to 35 C., the FTIR spectrometermust maintain a modulation efficiency of 50% or better. Modulationefficiency is a measure of the useful signal produced by the FTIRspectrometer and is calculated by taking the ratio of the peakinterferogram value at zero path difference to the DC value and thenmultiplying by 100. The maximum theoretical value of modulationefficiency is 100% with typical FTIR spectrometers achieving values inthe range of 65% to 95%. FTIR spectrometers with modulation efficienciesbelow 50% have relatively poorer SNR because of the additional Shotnoise from the larger proportion of non-signal bearing DC light fallingon the photodetector.

In addition to maintaining modulation efficiency at or above 50% overthe ambient temperature operation range, the FTIR spectrometer's changein percent transmittance (% T) at wavelengths between 1.2 and 2.5microns (8000 to 4000 cm⁻¹) should not exceed 2% per degree Celsius.This maximum temperature sensitivity is necessary to preserve theglucose net analyte SNR and to simplify calibration maintenance.

The spectral shape changes induced by thermal drift of the FTIRspectrometer should be simple in shape such that they do notsignificantly degrade the glucose net analyte signal. One method ofquantifying thermal drift for the FTIR subsystem and/or the entiresystem is to place the device in a temperature controlled chamber andthen measure spectra from a stable reference sample, such as anintegrating sphere, as a function of time and temperature change in thechamber. A principle components analysis can be performed on theresulting absorbance spectra from the experiment and 99.99% of thevariance due to thermal changes should be explained in the first 5 eigenvectors from the principle components analysis. In addition, the % Tchange with temperature can be calculated from the data set, and thecalculated temperature coefficient should be 2% per degree Celsius orless.

As previously stated, the FTIR output beam 245 is sent to a condensingoptical element or elements 244 that focus the light onto a NIRsensitive detector. The condensing element or elements 244 can berefractive and/or reflective in nature. Examples of NIR detectors thatare sensitive in the spectral range of 1.2 to 2.5 μm include InGaAs,InAs, InSb, Ge, PbS, and PbSe. In the art, non-invasive glucose monitorshave been described that utilize standard and extended range InGaAsdetectors that are sensitive from 1.2 to 1.7, 1.9 or 2.1 μm. Inaddition, liquid nitrogen cooled InSb detectors have been used. Also,PbS and PbSe detectors have been used. The present invention is uniquein that it utilizes a thermo-electrically cooled, extended range InGaAsdetector that is sensitive to light in the 1.2 to 2.5 μm range. The 2.5μm, extended range InGaAs detector has low Johnson noise and, as aresult, allows Shot noise limited performance for the photon fluxemanating from the illumination/tissue sampler/FTIR spectrometersubsystems. The extended InGaAs detector has peak sensitivity in the 2.0to 2.5 μm spectral region where three very important glucose absorptionpeaks are located. Unlike the liquid nitrogen cooled InSb detector, thethermo-electrically cooled, extended range InGaAs is practical for acommercial product. Also, this detector exhibits over 120 dbc oflinearity in the 1.2 to 2.5 μm spectral region.

Any photodetector may be used with the present invention as long as thegiven photodetector satisfies basic sensitivity, noise and speedrequirements. A suitable photodetector must have a shunt resistancegreater than 6000 ohms, a terminal capacitance less than 6 nano faradsand a minimum photosensitivity of 0.15 amps per watt over the 1.2 to 2.5micron spectral region. In addition, the photodetector must have acut-off frequency greater than or equal to 1000 hertz. The shuntresistance of the photodetector defines the Johnson or thermal noise ofthe detector. The Johnson noise of the detector must be low relative tothe photon flux at the detector to ensure Shot noise limited performanceby the detector. The terminal capacitance governs the cut-off frequencyof the photodetector and may also be a factor in the high frequencynoise gain of the photodetector amplifier. The photo sensitivity is animportant factor in the conversion of light to an electrical current anddirectly impacts the signal portion of the SNR equation.

The optical interferogram is converted to an electrical signal by thedetector and this signal is received by the data acquisition subsystem500. The data acquisition subsystem 500 amplifies and filters theelectrical signal from the detector and then converts the resultinganalog electrical signal to its digital representation with an analog todigital converter. The analog electronics and ADC must support the highSNR and linearity inherent in the interferogram. In order to preservethe SNR and linearity of the interferogram, the data acquisitionsubsystem 500 supports at least 100 dbc of SNR plus distortion. The dataacquisition subsystem 500 produces a digitized interferogram that hasuniform spatial displacement between samples. The data acquisitionsubsystem 500 also receives the reference laser signal from the FTIRspectrometer subsystem 400. Both the NIR signal and the reference laserare digitized by a 24-bit delta-sigma ADC operated at 96 kilohertz. Thedigital output of the ADC is processed by a signal processor to producean interferogram that is sampled at constant spatial intervals. Theinterferograms are passed to the embedded computer subsystem 600 forfurther processing, as discussed below. Traditionally, the zerocrossings of the reference laser are utilized to mark constant spatialintervals for sampling of the interferogram. The zero crossings of thereference laser are spaced at intervals equal to half the wavelength ofthe monochromatic light emitted by the laser.

Further, the data acquisition subsystem 500 utilizes a constant timesampling, dual channel, delta-sigma analog-to-digital converter (ADC) tosupport the SNR and photometric accuracy requirements of the presentnon-invasive glucose measurement. In preferred embodiments, thedelta-sigma ADC utilized supports sampling rates of over 100 kHz perchannel, has a dynamic range in excess of 117 dbc and has total harmonicdistortion less than −105 dbc.

There are other types of data acquisition systems for the FTIRspectrometer and photodetector that are well known in the art and couldbe employed in the present invention if they provide the followingperformance characteristics for constant spatial sampling, dynamicrange, SNR, harmonic distortion and sampling speed. There is anallowable error in determining the constant spatial sampling intervalsof the interferogram, and the spatial sampling interval determinationmust have a maximum spatial sampling jitter of +/−25 nanometers in orderto preserve a SNR of 100 dbc at 1.2 microns (8000 cm⁻¹). Levels ofspatial sampling jitter greater than +/−25 nanometers will introducefrequency modulation artifacts into the spectral and will degrade theglucose net analyte signal. In addition, the data acquisition subsystemmust support a dynamic range of at least 100 dbc, a SNR of 90 dbc andhave total harmonic distortion less than 90 dbc. Finally, the ADC of thedata acquisition subsystem must be able to sample at speeds of 5,000samples per second or greater in order to support a minimum FTIR movingmirror scanning speed of 0.25 centimeters per second.

The constant time sampling data acquisition subsystem 500 has severaldistinct advantages over the more traditional methods of acquiringinterferograms from an FTIR spectrometer. These advantages includegreater dynamic range, lower noise, reduced spectral artifacts, detectornoise limited operation and simpler and less expensive analogelectronics. In addition, the constant time sampling technique improvesthe vibration immunity of the FTIR because it can digitally compensatefor delay mismatches between the laser reference and infrared detectorsand can back out the non-ideal aspects of the electronics' transferfunction. The main disadvantages of the constant time sampling techniqueare the increased computational and memory requirements necessary totranslate the constant time samples of the interferogram to constantspatial samples. With the use of a high performance digital signalprocessor (DSP), the additional computation and memory requirements areeasily outweighed by the performance enhancements of the constant timesampling technique.

The data acquisition subsystem passes the digitized, constant spatiallysampled interferograms to the embedded computer subsystem 600 forfurther processing. The embedded computer subsystem 600 converts thestream of interferograms to single beam spectra by windowing theinterferogram, performing phase correction of the windowed interferogramand then taking the Fourier transform of the windowed and phasecorrected inteferogram. Either Mertz or power phase correction methodsmay be used. The power phase correction method is simpler to implement,but results in noise that has non-zero mean and is larger in magnitudeby a factor of 1.414. The Mertz phase correction method is morecomplicated but produces noise with zero mean and does not inject noisefrom the imaginary portion of the complex spectrum. The Mertz methodresults in spectra with higher photometric accuracy, however, when usingmultivariate analysis techniques, both phase correction methods resultin equivalent prediction performance.

After converting the interferograms to single beam spectra, the embeddedcomputer system will preferably check the single beam spectra foroutliers or bad scans. An outlier sample or bad scan is one thatviolates the hypothesized relationship between the measured signal andthe properties of interest (i.e., noninvasive measurement of glucoseconcentration in human tissue). Examples of outlier conditions includeconditions where the calibrated instrument is operated outside of thespecified operating ranges for ambient temperature, ambient humidity,vibration tolerance, component tolerance, power levels, etc. Inaddition, an outlier can occur if the composition or concentration ofthe sample is different than the composition or concentration range ofthe samples used to build the calibration model. Any outliers or badscans will be deleted and the remaining good spectra are averagedtogether to produce an average single beam spectrum for the measurement.The average single beam spectrum is then preferably converted toabsorbance by taking the negative base 10 logarithm (log10) of thespectrum. The absorbance spectrum is then preferably scaled by a singlebeam spectrum to renormalize the noise. The resulting scaled absorbancespectrum will then have calibration maintenance and/or calibrationtransfer algorithms applied to it. Calibration maintenance techniquesare discussed in detail below and in commonly assigned U.S. patentapplication Ser. No. 09/832,608, filed on the same date herewith andentitled “Optically Similar Reference Samples and Related Methods forMultivariate Calibration Models Used in Optical Spectroscopy”, thedisclosure of which is incorporated herein by reference. Calibrationtransfer techniques are disclosed in commonly assigned U.S. patentapplication Ser. No. 09/563,865, filed May 3, 2000, entitled “Method andApparatus for Spectroscopic Calibration Model Transfer”, the disclosureof which is incorporated herein by reference. Finally, a tailoringalgorithm such as that disclosed in U.S. Pat. No. 6,157,041 is appliedto the spectrum to remove inter-patient variation. After the tailoringstep, outlier detection can be performed on the spectrum to check theconsistency of the spectrum with the spectra used to generate themultivariate calibration. If the spectrum is consistent with themultivariate calibration spectra, final regression coefficients of thecalibration model are applied to the spectrum to produce a glucoseprediction. In preferred embodiments, the glucose concentration valuefrom the tailoring spectrum is added to the predicted glucose value toproduce the actual glucose concentration for the subject.

To better appreciate the benefits afforded by the calibrationmaintenance subsystem 300, it is useful to analytically review theproblem at hand. The problem solved by the calibration maintenancesubsystem is the difficulty in maintaining a multi-wavelengthcalibration model for quantitatively measuring the concentration ofanalytes whose spectral absorption is much smaller than that of thegross sample spectrum. The cause of the failure of a spectrallydissimilar reference sample to maintain calibration under theseconditions can be described analytically as shown below.

It has been shown in the literature that photometric inaccuracies willbe present even in an ideal instrument of finite resolution where allsources of non-linearity (detector response, stray light, etc.) havebeen removed. See, for example, R. J. Anderson and P. R. Griffiths,Analytical Chemistry, Vol. 47, No. 14, December 1975; and R. J. Andersonand P. R. Griffiths, Analytical Chemistry, Vol. 50, No. 13, November1978. This inherent inaccuracy is caused by the finite resolution of theinstrument (grating spectrometer or FT interferometer) because aspectrum produced by an instrument with finite resolution will be thetrue sample spectrum convolved with the instrument line shape (ILS) (fora grating spectrometer, the ILS is a function of the entrance and exitslit widths and for an FT interferometer, the ILS is a function of theinstrument self-apodization and the apodization function used inperforming the Fourier transform). One can think of the convolutionprocess as a distortion of the true spectrum at a particular wavenumberthat is dependent on all other spectral intensities within the spectralbandpass of the instrument. Mathematically this can be written asEquation (10): $\begin{matrix}{{T^{a}\left( {\overset{\_}{v}}_{i} \right)} = {\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K{(\overset{\_}{v})}}}l}\quad {\overset{\_}{v}}}}} & {{Eq}.\quad (10)}\end{matrix}$

where T^(a)({overscore (ν)}_(i)) is the measured (or apparent)transmission at a particular optical frequency, {overscore (ν)}_(i), σdefines the ILS (or apodization), K({overscore (ν)}_(i)) is theabsorption coefficient of the species being observed and l is thepathlength through the sample. A conclusion drawn from the Griffithspaper is that this apodization induced distortion causes significantdeviations from Beer's law when the true absorbance of a peak exceeds0.7 AU.

The referenced literature also shows, and it can be inferred fromEquation (10), that deviations from Beer's law are also a function ofthe instrument resolution relative to the narrowness of the spectralline being measured. A quantity called the resolution parameter, ρ, isdefined as the ratio of the instrument resolution, R, to thefull-width-half-height (FWHH) of the spectral band of interest as setforth by Equation (11):

ρ=R/FWHH   Eq. (11)

The effect of ρ on photometric accuracy can be understood in the limitby examining Equation (10). If the ILS is thought of as a Dirac-delta orimpulse function (i.e., perfect instrument resolution), then the ILSconvolution in Equation (1) yields the absorbance term independent ofILS, in other words the true absorbance spectrum is measured if theinstrument operates with infinite resolution. On the other hand, if theabsorbance term is thought of as a delta function, we are left with onlythe ILS centered at the discrete wavelength where the absorption lineoccurs. One can then summarize from the referenced literature thatphotometric inaccuracy due to apodization is a function of both ρ andthe spectral absorbance of the sample as set forth in Equation (12):

Error=ƒ{ρ,A ^(T)({overscore (ν)})}  Eq. (12)

where A^(T)({overscore (ν)}) is the true absorbance of all absorbers inthe sample.

It will be shown below that when there are different absorbers in thesample and background (for example, liquid water, glucose and watervapor in the sample and only water vapor in the background), thebackground usually does not capture a system perturbation in the sameway that the sample will record the same perturbation. The strategy forusing a background in spectroscopy is to capture and correct forinstrumental or environmental variations so that the true absorbers inthe sample can be identified. If the coefficients of absorption areincluded for all absorbers in the system, Equation (10) can be rewrittento represent the measured transmission of any sample in any environment.For the particular case of glucose in water in the presence of watervapor, Equation (10) becomes Equation (13): $\begin{matrix}{{T_{s}^{A}\left( {\overset{\_}{v}}_{i} \right)} = {\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{g}{(\overset{\_}{v})}}}l_{g}}^{{- {K_{w}{(\overset{\_}{v})}}}l_{w}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}}}} & {{Eq}.\quad (13)}\end{matrix}$

where the subscript “I” represents instrument, “g” represents glucose,“w” represents liquid water and “v” represents water vapor present inthe measuring environment. A typical background sample spectrumcontaining no glucose or water would be written as Equation (14):$\begin{matrix}{{T_{b}^{A}\left( {\overset{\_}{v}}_{i} \right)} = {\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}}}} & {{Eq}.\quad (14)}\end{matrix}$

where the background spectrum measures the instrument absorbance and thewater vapor absorbance. The background corrected sample spectrum wouldbe written as Equation (15): $\begin{matrix}{\frac{T_{s}^{A}\left( {\overset{\_}{v}}_{i} \right)}{T_{b}^{A}\left( {\overset{\_}{v}}_{i} \right)} = \frac{\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{g}{(\overset{\_}{v})}}}l_{g}}^{{- {K_{w}{(\overset{\_}{v})}}}l_{w}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}}}{\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}}}} & {{Eq}.\quad (15)}\end{matrix}$

As in Equation (10), the spectral intensity at each optical frequencydepends on the spectral intensity of the adjacent frequencies measuredby the instrument, the absorption terms for the instrument e^(−K)^(_(l)) ^(({overscore (ν)})l) ^(_(i)) and the water vapor e^(−k)^(_(vl)) ^(({overscore (ν)})l) ^(_(ν)) do not cancel in Equation (15),resulting in a background corrected spectrum that is not equal to thetrue absorbance spectrum of the measured analytes. The only way theseterms will ever cancel is if all other absorption terms that are notcommon to both sample and background are negligible or do not vary withoptical frequency. Equation (15) can be expanded further to encompassany instrumental or environmental perturbation from the calibrationstate as set forth by Equation (16): $\begin{matrix}{\frac{T_{s + \Delta}^{A}\left( {\overset{\_}{v}}_{i} \right)}{T_{b + \Delta}^{A}\left( {\overset{\_}{v}}_{i} \right)} = \frac{\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{g}{(\overset{\_}{v})}}}l_{g}}^{{- {K_{w}{(\overset{\_}{v})}}}l_{w}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}^{{- {K_{\Delta}{(\overset{\_}{v})}}}l_{\Delta}}}}{\int_{0}^{\infty}{\sigma \quad \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}^{{- {K_{\Delta}{(\overset{\_}{v})}}}l_{\Delta}}}}} & {{Eq}.\quad (16)}\end{matrix}$

where the subscript Δ represents the absorption due to the perturbation.Maintenance of calibration could be achieved using any reference sampleif the ratio in Equation (16) were equal to the ratio in Equation (15).However, as long as the unknown sample and reference sample havedifferent spectral characteristics, Equation (16) will never identicallyequal Equation (15). The two equations become more similar as thereference sample begins to absorb more like the prediction sample.

In summary, a similar background is required when the systemperturbation is not well modeled and the perturbation is not negligiblein magnitude compared to the absorbers in the prediction sample, or whenthe spectral resolution (full width at half height) of the perturbationis much less than the instrument resolution. Another way to write thisrequirement is in terms of the final regression coefficients from amultivariate calibration model acting on the spectrum of the unknownsample. This can be written as Equation (17):

{right arrow over (F)}·({right arrow over (S)}₀+{right arrow over(S)}_(NL)+{right arrow over (ε)}){right arrow over (F)}·{right arrowover (S)}_(NL)<<{right arrow over (F)}·{right arrow over (ε)}  Eq. (17)

where {right arrow over (F)} represents a vector of final regressioncoefficients, {right arrow over (S)}₀ represents the true spectrum,{right arrow over (S)}_(NL) represents the distorted, or non-linear,part of the measured spectrum due to the finite resolution of theinstrument and {right arrow over (ε)} represents the spectral error dueto sources of random error. In other words, the product of the finalregression coefficients and the non-linear portion of the measuredspectrum caused by a system perturbation should be much less than theproduct of the final regression coefficients and the random errorpresent in the measured spectrum so that the error term due to thedistorted part of the spectrum is small and prediction performance ismaintained.

There are several different types of instrumental and environmentalvariation which may affect the prediction capability of a calibrationmodel. It is possible, and highly desirable to reduce the magnitude ofthe effect of instrumental and environmental variation by incorporatingthis variation into the calibration model. It is difficult, however, tospan the entire possible range of instrument states during thecalibration period. System perturbations can result in the instrumentbeing operated outside the space of the calibration model. Measurementsmade while the instrument is in an inadequately modeled state willexhibit prediction errors which render the measurement useless. In thecase of in vivo optical measurements, these types of errors may resultin erroneous medical information being used for the treatment ofpatients. These errors are obviously unacceptable in a commercialdevice.

Some examples of problematic instrument and environmental variationinclude, but are not limited to: changes in the levels of environmentalinterferents such as water vapor or CO₂ gas, changes in the alignment ofthe instrument's optical components, fluctuations in the output power ofthe instrument's illumination system, and changes in the spatial andangular distribution of the light output by the instrument'sillumination system. It will be shown through both simulated andempirical results that a spectrally similar background sample providesimproved capability to correct for these types of variations.

Correcting for any of the classes of instrument and environmentalvariation requires that the background sample have matched spectralabsorption features with the sample of interest. It has already beenshown mathematically that the finite instrument resolution causes theeffect of different instrument states to depend on the spectralabsorption characteristics of the sample (Equation (16)). Another way ofstating this problem is that the optical effects of instrument andenvironmental variation should ideally be identical in both thebackground sample and the sample of interest. Taking the derivative ofEquation (13) with respect to water vapor absorption yields Equation(18): $\begin{matrix}{\frac{{T_{s}^{A}\left( {\overset{\_}{v}}_{i} \right)}}{{K_{v}\left( \overset{\_}{v} \right)}} = {\int_{0}^{\infty}{{- l_{v}}{\sigma \left( {\overset{\_}{v} - {\overset{\_}{v}}_{i}} \right)}^{{- {K_{I}{(\overset{\_}{v})}}}l_{I}}^{{- {K_{g}{(\overset{\_}{v})}}}l_{g}}^{{- {K_{w}{(\overset{\_}{v})}}}l_{w}}^{{- {K_{v}{(\overset{\_}{v})}}}l_{v}}\quad {\overset{\_}{v}}}}} & {{Eq}.\quad (18)}\end{matrix}$

It is apparent from Equation (18) that the spectrum of water vapor ismodified by the spectral shape of all compounds in the sample. Thisrelationship holds true for any system perturbation which causes achange in the optical appearance of a sample's spectrum.

Simulated results are presented for the effects of water vapor levelvariation on the in vitro measurement of glucose in reflectance usingscattering media. Actual spectra from 98 glucose solution samples werecollected using an FTIR spectrometer operated at 16 cm⁻¹ resolution. Thesamples contained variable levels of scattering media to simulateoptical pathlength distributions similar to those seen in living tissue.For comparison purposes, spectra from two different types of backgroundsamples were also collected: a similar background with matched opticalproperties and an air background (i.e., an integrating sphere placedover the reflectance sampler). High-resolution water vapor spectra(obtained at 1 cm⁻¹) were then artificially added to the solution andbackground spectra in order to simulate varying water vapor levels.Simulations were run on the resulting spectra in order to model theeffects of finite instrument resolution on the added interferents. Thesample spectra were then ratioed to the background sample spectra in anattempt to remove the effects of the varying water vapor levels. FIG. 34shows the residual spectral effects after this background correction wasperformed. The two plots in FIG. 34 show the remaining spectraldifferences when the ratioed spectra with added water vapor aresubtracted from the original ratioed spectra without added water vapor.As can be seen in the figure, the spectrally similar background reducesthe effects of the water vapor interferent by a significant amount. Acalibration developed at a constant water vapor was used to predict onthe sample spectra. As stated above, the sample spectra were ratioedagainst a similar background with matched optical properties and an airbackground. The prediction errors for the sample data with the airbackground ratio were inflated over the sample spectra with a similarbackground by approximately 40 mg/dl using a calibration model with 20factors. This simulation clearly demonstrates the advantage of using asimilar background for correcting for even simple system perturbations.

Many of the types of instrument variation involve interactions with thesampling geometry of the sample. These types of instrument variationinclude changes in alignment of optical components and changes inangular and spatial distribution of the output light from theinstrument's illumination system. These types of variations may becaused by a number of physical mechanisms, including: aging of opticalmounts, thermally induced mechanical deformations of optical mounts,aging of light sources, or variations in routinely replaced componentssuch as light bulbs. In order to be effective, the background samplemust preserve the same mapping of angular and spatial distribution oflight as the sample of interest. This requires that the backgroundsample interact with the sampling optics of the instrument in a mannerthat mimics the interaction of the sampling optics with the sample ofinterest.

An additional constraint which is generally required for successfulcalibration maintenance is that the overall intensity of light seen atthe optical detector elements be closely matched for both the backgroundsample and the sample of interest. This constraint helps to correct fornon-linearities in the instrument's optical measurement characteristics.Again, this constraint is included in the overall definition of similarspectral radiance.

Empirical results are presented for an actual, in vivo study measuringblood glucose concentrations non-invasively. The study was intentionallydesigned to include several of the types of instrument and environmentalvariation previously discussed herein. Specifically, ambient relativehumidity, ambient temperature, and illumination power were all variedduring the prediction phase of the study. This study was intended as aproof of concept for using a similar background reference sample forcalibration maintenance. The study was limited to five subjects over aperiod of two days. Prediction errors were determined by comparingnon-invasive results to standard capillary blood glucose referencemeasurements. FIG. 35 demonstrates the superior ability of the similarbackground to maintain the prediction performance of the calibration inthe presence of instrument and environmental variation by generating alower standard error of prediction and by generating the smoothestdecreasing SEP curve. FIG. 36 shows the spectral differences between themean human tissue spectrum and the two different background sample typesbeing tested in the study.

Refer now to FIG. 37 which illustrates a flowchart for determiningspectral similarity. The spectral similarity of an optically similarreference sample to the test sample of interest may be quantified withrespect to spectral absorbance, mapping of input to output light spatialdistribution, and mapping of input to output light angular distribution.

There are two metrics that may be used to calculate the similarity of aparticular background sample to the sample of interest with respect tospectral absorbance. The first involves comparing the optically similarreference sample in question to the test samples, typically tissuespectra, where all of the background and tissue spectra were collectednear in time, as set forth in Equation 19: $\begin{matrix}{\text{Spectral~~Similarity} = \frac{\sum\limits_{i = 1}^{I}\quad \left( {\sum\limits_{j = 1}^{J}\quad \left( {X_{ij} - z_{i}} \right)^{2}} \right)}{I}} & {{Eq}.\quad (19)}\end{matrix}$

where X is a set of tissue pseudo-absorbance spectra and z is any meanbackground pseudo-absorbance spectrum for the time in question. (Thepseudo-absorbance spectrum is defined in Equation 20). I refers to thetotal number of data points collected in the wavelength region ofinterest (or the total number of discrete wavelengths chosen foranalysis), and J refers to the total number of tissue spectra collectedin this period of time. The average value of the spectrum should besubtracted from all wavelengths before calculating the metrics. Thisstep ensures that the spectral shapes of the background and tissue arecorrectly compared without being influenced by a uniform, DC energyoffset or baseline shift.

Pseudo-absorbance=−log₁₀(I)   Eq. (20)

where I is a single beam intensity spectrum.

Quantifying the degree of spectral similarity can be done through astraightforward process involving a comparison between the spectra inwhich the analyte is to be measured and the optically similar referencesample. The flowchart of FIG. 37 summarizes this process. The processinvolves the following steps:

Step #1: Define or establish the representative measurement sample. Arepresentative measurement sample is a sample that is representative ofsamples on which the optical measurement system will be makingsubsequent measurements. If the application is a single patient withdiabetes, then a representative measurement sample would be a sample atthe sampling location on that patient. If the application group is aheterogeneous group of subjects, then the representative measurementsamples would be an appropriate group of subjects on which the monitorwould be subsequently used. If the measurement group were othersub-populations of subjects, then the representative measurement sampleswould be obtained from the sub-population. For example, in patients withrenal disease, the representative measurement population would bepatients with renal disease.

Step #2: Obtain spectral measurements from the representativemeasurement samples. In all cases, multiple measurements withreinsertions of the tissue into the sampling device should be made. Inthe case of a single subject application, at least ten spectralmeasurements should be made. In the case of a heterogeneous patientpopulation, the representative measurement samples should be areflection of the subjects that will subsequently use the monitor. Inthe example below, 30 subjects of varying ages, gender, ethnicity andbody mass index were used. The spectral measurements should be made in amanner consistent with use of the monitoring device. These spectra arehereafter referred to as the representative measurement spectra.

Step #3: Calculate a mean pseudo-absorbance spectrum from the spectraobtained from the representative measurement samples. The resultingspectrum is hereafter referred to as the mean representative measurementspectrum.

Step #4: Obtain spectral measurements from the optically similarreference sample. In all cases, multiple insertions and measurements ofthe optically similar reference sample should be made. It is preferredthat at least 10 measurements should be made. These spectra arehereafter referred to as the optically similar reference sample spectra.

Step #5: Calculate a mean pseudo-absorbance spectrum from the opticallysimilar reference sample spectra. The resulting spectrum is hereafterreferred to as the mean optically similar reference spectrum.

Step #6: Use the representative measurement spectra and the meanrepresentative measurement spectrum with Equation (19) to calculate aspectral similarity value. The resulting value will hereafter bereferred to as the spectral similarity measure #1.

Step #7: Use the representative measurement spectra and the meanoptically similar reference spectrum with Equation (19) to calculate aspectral similarity value. The resulting value will hereafter bereferred to as the spectral similarity measure #2.

Step #8: Ratio the two spectral similarity values to obtain a spectralsimilarity ratio.$\text{Spectral~~similarity~~ratio} = \frac{\text{Spectral~~Similarity~~Measure~~\#2}}{\text{Spectral~~Simiarity~~Measure~~\#1}}$

Equation (19) is a mean sum of squares (SS) metric, and it may becalculated for different wavelength regions. It may be calculated for acontinuous spectral region, for discrete wavelengths, for combinationsof two or more discrete wavelengths (which may or may not have beenfound using a variable selection algorithm), or for selected regions ofa spectrum.

Table 5 below shows the values that were calculated for Equation (19)for a representative group of subjects for three levels of similarity:acceptable, preferred, and ideal. The spectral regions and discretewavelengths for which these values were calculated are also indicated inthe table. The discrete variables used in this case are glucoseimportant wavelengths and are specified in Table 6. The more similar thebackground is to the tissue spectra, the smaller the SS value becomes.Table 7 shows the same spectral similarity metrics when therepresentative sample is a single subject.

TABLE 5 Spectral Similarity Ratio Absorbance Troughs (4,440 cm⁻¹-Example Full Spectrum 4,800 cm⁻¹ & Level of Background (4,200 cm⁻¹-Discrete 5,400 cm⁻¹- Similarity Sample 7,200 cm⁻¹) Variables 6,400 cm⁻¹) Acceptable Scattering 30 30 30 Solutions Preferred Transmission 1010 10 Cell Ideal Mean 1 1 1 Subject Spectrum

TABLE 6 Glucose-important variables (cm⁻¹) used in spectral similaritycalculations 4196 4451 4883 5369 5731 6163 6696 4227 4459 4922 5392 57556187 6935 4273 4497 5014 5454 5785 6287 6973 4281 4528 5091 5469 58096318 7004 4304 4559 5176 5477 5839 6349 7043 4320 4613 5230 5515 58936449 7066 4335 4690 5269 5585 5924 6472 7205 4366 4775 5299 5623 59476557 4389 4829 5315 5662 6001 6595 4436 4860 5338 5701 6094 6673

TABLE 7 Spectral Similarity Ratio Absorbance Troughs (4,440 cm⁻¹-Example Full Spectrum 4,800 cm⁻¹ & Level of Background (4,200 cm⁻¹-Discrete 5,400 cm⁻¹- Similarity Sample 7,200 cm⁻¹) Variables 6,400 cm⁻¹) Acceptable Scattering 1500 1500 30 Solutions Preferred Transmission1000 1000 10 Cell Ideal Mean 1 1 1 Subject Spectrum

If an analyte is to be determined, it is helpful if the backgroundmatches different regions and/or discrete wavelengths of the spectrumthat are important in the determination. In other words, if spectralregion A is important in determining the analyte, then the backgroundshould match the tissue especially well in region A. On the other hand,region A may not be at all important in determining a different analyte,in which case the spectral match would be less important for thatregion. When an analyte is to be determined, therefore, another metricmust also be defined that is specific to the analyte is question, asshown in Equation (21) below. $\begin{matrix}{\text{Regression~~weighted~~Similarity} = \frac{\sum\limits_{i = 1}^{I}\quad \left( {\sum\limits_{j = 1}^{J}\quad \left( {{b_{i}*X_{ij}} - {b_{i}*z_{i}}} \right)^{2}} \right)}{I}} & {{Eq}.\quad (21)}\end{matrix}$

where b is the regression vector for the analyte being determined,normalized to length one, and the other symbols have the same meaningsas in Equations (19) and (20). This regression vector may be calculatedvia any linear or non-linear regression method, where partial leastsquares is an example of such a method. It may be thought of as theanalyte's calibration model, and it weights the absorbances at differentwavelengths based on their importance in predicting the analytecharacteristic of interest.

The process for quantifying the degree of spectral match is the sameexcept that Equation (21) is used instead of Equation (19). The 8-stepprocess is the same with a single substitution of the equations. Theresulting ratio will hereafter be referred to as the regression weightedspectral similarity ratio.

Table 8 shows results from Equation (21), calculated for arepresentative group of subjects when the analyte of interest wasglucose; however, these values may also be calculated for any componentin the sample that is to be determined. It can be seen that the idealbackground has a much smaller SS value than the acceptable background,since it is more similar to tissue spectra collected during the sameperiod of time. The more similar the background is, the smaller the SSvalue will be for Equation (19) or Equation (21) or both, for anyspectral region or any combination of regions or any discrete wavelengthor combination of discrete wavelengths. Table 9 shows the same spectralsimilarity metrics when the representative sample is an individualsubject. In an analysis where no specific characteristic (e.g.,concentration) of the sample is being measured, then Equation (19) issufficient. When a specific characteristic is to be determined, however,both Equations (19) and (21) may be evaluated.

If the spectral similarity ratio for the optically similar referencesample value is less than 30, then the optically similar referencesample is to be considered an acceptable optically similar referencesample. If the spectral similarity ratio is less than 10, then theoptically similar reference sample is to be considered a preferredoptically similar reference sample. If the spectral similarity ratio isless than or equal to 1, then the optically similar reference sample isto be considered an ideal optically similar reference sample. Themetrics must be calculated for the analyte being determined and for thewavelengths/wavelength regions being used to ensure the validity of thesimilarity determination.

TABLE 8 Regression Weighted Spectral Similarity Ratio Absorbance Troughs(4,440 cm⁻¹- Example Full Spectrum 4,800 cm⁻¹ & Level of Background(4,200 cm⁻¹- Discrete 5,400 cm⁻¹- Similarity Sample 7,200 cm⁻¹)Variables 6,400 cm ⁻¹) Acceptable Scattering 30 30 30 SolutionsPreferred Transmission 10 10 10 Cell Ideal Mean 1 1 1 Subject Spectrum

TABLE 9 Regression Weighted Spectral Similarity Ratio Absorbance Troughs(4,440 cm⁻¹- Example Full Spectrum 4,800 cm⁻¹ & Level of Background(4,200 cm⁻¹- Discrete 5,400 cm⁻¹- Similarity Sample 7,200 cm⁻¹)Variables 6,400 cm ⁻¹) Acceptable Scattering 4500 3000 9000 SolutionsPreferred Transmission 1500 2500 3000 Cell Ideal Mean 1 1 1 SubjectSpectrum

The similarity of the mapping function of light spatial distribution andlight angular distribution can also be quantified for optically similarreference samples. The preferred method for quantifying the similarityof these properties is to examine the image of the output light beam,which is produced after the light has passed through the sampling opticsand the sample of interest. For purposes of this discussion, the lightbeam is assumed to be circular in cross-section, but the similaritymetrics can be extended to any geometry of beam (e.g., the output of asquare cross-section light guide). The boundary of the light beampassing through the sample is defined as the points at which the lightintensity falls to 1/e² times the peak light intensity.

The image of the output beam is measured using any standard intensitymapping scheme (e.g., scanning a single pixel detector or using a CCDcamera) and using a goniometer. This allows both the spatial and angulardistributions of the light beam to be determined. Measurements should bemade for both the sample of interest and for the similar backgroundbeing quantified. In order to standardize the calculation for manyapplications, the image should be divided into approximately one hundredequally sized “bins” (or squares), with ten bins across the diameter ofthe image. This can be accomplished by either measuring the beam in aten by ten grid or by sampling at a finer spacing and then averaging thedata. The spatial and angular distributions for the sample of interestare then subtracted from the corresponding distributions of thebackground sample. The resulting images represent the similarity levelfor the background and the sample of interest. In order to quantify thissimilarity, all of the data points in the image are put into a vectorfor easier calculation, and the vector is normalized so that its lengthequals 1. This is achieved by dividing each data point in the image bythe 2-norm (∥x∥₂), which is equivalent to the Euclidean distance of thevector. $\begin{matrix}{{x}_{2} = \left( {\sum\limits_{i = 1}^{n}\quad {x_{i}}^{2}} \right)^{1/2}} & {{Eq}.\quad (22)}\end{matrix}$

where x is the vector of the difference image and n is the number ofdata points in that vector.

The normalization step ensures that the magnitude of everydifference-image is comparable. Following the normalization step, thestandard deviation of the normalized image vector is calculated, andthis metric is an indication of how similar the background and sampleimages are. Table 9 shows the standard deviations that are ideal,preferred and acceptable for the spatial distribution of similarbackgrounds. Table 10 shows the same metrics for angular distribution.

TABLE 10 Level of Spatial Similarity Metric Similarity (StandardDeviation) Acceptable 0.079 Preferred 0.052 Ideal 0

TABLE 11 Level of Angular Similarity Metric Similarity (StandardDeviation) Acceptable 0.051 Preferred 0.036 Ideal 0

As stated previously, the optically similar reference sample is used tocapture the current instrument state such that the effect ofinstrumental and environmental variation on prediction capability can beeliminated. There are several different methodologies by which thereference spectrum can be used to correct for instrumental andenvironmental variation. These spectral correction methods include, butare not limited to those described below.

These correction methodologies can be classed into two broad categories:methods which modify the spectrum of the test sample and methods whichmodify the calibration model. The simplest and preferred method modifiesthe spectrum of the sample of interest by subtracting the opticallysimilar reference spectrum in absorbance space. The reference spectrummay be the most recently collected optically similar reference spectrum,or it may be an averaged spectrum containing information from severalbackground samples collected at different points in time. One preferredmethod of averaging is to exponentially time weight the backgroundreference spectra and average them together. The exponentially timeweighted method allows for the optimization of achieving highsignal-to-noise-ratio correction data and capturing the currentinstrument state.

The second class of background correction methodologies consists ofactually modifying the multivariate calibration model. One simple methodis to simply include the reference spectra with the original calibrationsamples and rerun the regression algorithm on the combined data set. Apreferred method is to include only the spectral variation from thebackground reference sample in the calibration model. This methodconsists of taking multiple background reference samples during thecalibration period, finding the mean of the background reference samplespectra collected during the calibration period, subtracting (inabsorbance space) this mean background reference spectrum fromsubsequent background reference spectra collected prior to making anactual prediction, adding this spectral difference back to thecalibration samples, and rerunning the regression algorithm to create anupdated calibration model. In an alternative method, a PCA decompositionis run on the spectral differences seen in the background and a limitednumber of eigenvectors is used to add this spectral variation back tothe model.

Referring now to FIGS. 38-55, several embodiments of similar backgrounddevices for use in a calibration maintenance subsystem 300 of thepresent invention are depicted. Each of the similar backgroundembodiments discussed may be used in combination within the presentsystem. These specific backgrounds are intended for applications, suchas glucose measurement, in which analyte concentrations are to bemeasured in vivo using reflection spectroscopy. Specifically, theseoptically similar reference samples are designed to match the opticalproperties of tissue at selected wavelengths in the near infrared regionincluding 1.2 to 2.5 μm (8000 to 4000 wavenumbers). In this opticalregion, water is the dominant absorbing component contained in thetissue. Each of the following backgrounds is designed to providemultiple optical pathlengths through water in order to mimic thespectrum of living tissue. Based upon Monte Carlo simulations of lightpropagation through scattering media where the scattering propertiesmatch those of tissue, a distribution of pathlengths can be calculated.The results can be defined by a mean pathlength with a standarddeviation and skew to the distribution. The distribution skew is towardlonger pathlengths. Typically the standard is less than or equal to themean. For example, if the mean pathlength is 1 mm, then the standarddeviation of pathlengths is about 1 mm as well.

In developing and assessing reference samples, is important to have ametric that enables one to rapidly and easily determine if multipleoptical pathlengths of water are created by the reference sample. Onesimple way is to fit the absorbance spectrum of the reference samplewith three terms: 1) an offset, 2) a slope with wavenumber, and 3) thepure component of water. The pure component of water is simply theabsorbance of water at a fixed pathlength. Mathematically stated:

Â(x)=b ₀ +b ₁ x+b ₂ PC(x)   Eq. (23)

The three fitting parameters are estimated using a least squares fit ofthe above equation to the absorbance spectrum (which has no instrumentline shape in it). Following fitting of the above parameters thespectral residual is determined. The spectral residual is determined bysubtracting the above equation from the absorbance spectrum of thereference sample. The final step is to compute the root-mean-squared(RMS) error across the spectrum. $\begin{matrix}{{Multipath\_ RMSError} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\quad \left( {A_{i} - {\hat{A}}_{i}} \right)^{2}}}} & {{Eq}.\quad (24)}\end{matrix}$

The multipath RMS error is greater when multiple pathlengths of waterare present in the reference sample. A single pathlength sample willresults in a smaller RMS error then a two pathlength sample, etc. Asimple threshold value calculated in absorbance units can be used todetermine if multiple pathlengths of water are present. The threshold issensitive to the spectral region used. For, example the threshold wouldbe smaller if the region used for analysis had smaller absorbance bands.

Several novel designs are presented for achieving the multiple waterpathlengths required to match the spectrum of tissue. Most embodimentsconsist of an optical interface (e.g., an MgF₂ window) which is highlytransmissive in the optical region of interest, an optical samplingcompartment containing water, and diffusely reflective or scatteringmedia. For each background design, either experimental or simulated dataare presented showing how close a spectral match was achieved betweenthe background and human tissue.

The inventors recognize that in addition to including the dominantabsorbing species (e.g., water), the background sample may also includethe actual analyte of interest (e.g., glucose, ethanol, urea, etc.). Byincluding various analytes, the background sample may be used as aquality control or calibration sample in addition to its primary use inthe maintenance of calibration.

With specific reference now to FIGS. 38 and 39, a cone background device300 is illustrated in accordance with an embodiment of the presentinvention. FIG. 38 illustrates representative ray-traces in the conebackground device 301 and FIG. 39 illustrates a partial cut-away view ofthe cone background device 301. Cone background device 301 utilizes aconical geometry in order to help achieve some of the requiredperformance specifications for a background similar to human tissue. Itincludes an optically transparent cone 330 such as a fused silica cone,a thin layer of a constituent 320 such as water, collagen or lipid, anda diffusing cone 310 which provides approximately Lambertian reflectionof the incident radiation.

The cone geometry of device 301 provides excellent stray signalsuppression as best seen in the ray trace shown in FIG. 38. The usefulsignal is transmitted through the hollow portion 340 of the cone, andthen through the constituent layer 320. The amplitude of the signal thatis reflected back to the collection system without undergoing thedesired interaction is reduced significantly due to several Fresnelreflection losses. The useful radiation undergoes a randomizedreflection from the diffusing cone 310 surface, and passes back into theinner cone volume 340, either to be collected or to undergo yet anotherpass through constituent layer 320 and random reflection. FIG. 40 showsa graph of spectral response demonstrating the spectral match betweenthe tissue sample and the cone device 301.

The cone reference sample, as designed, contains a distribution ofoptical pathlengths through water. This distribution of waterpathlengths was confirmed by calculating the multipath RMS error in themanner explained above. The multipath RMS error was calculated over theregion of 4200-7200 cm⁻¹ and generated a value of 0.18 absorbance units.

Refer now to FIG. 41, which schematically illustrates a scatteringsolution background device 350 in accordance with another embodiment ofthe present invention. The scattering solution background 350 includes acontainer 352 that is at least partially optically transparent adjacentthe tissue sampling 12 and collection subsystem 14. The scatteringsolution background also includes a scattering solution 354. Scatteringsolution 354 comprises a plurality of reflective beads disposed in aliquid or gel constituent such as water, collagen or lipid. The randompathlength distribution of the scattering solution 354 is provided bythe reflective beads, which may comprise, for example, reflectingpolystyrene microbeads (0.298 μm diameter, 6600 mg/dl concentration) inaqueous solution. The particle reflectance, size and concentration ofthe reflective beads in the scattering solution 354 are set in order tocreate the desired match to tissue for the solution 354. Preferably, thesolution 354 is mechanically agitated by agitator 356 in order toprevent settling of the reflective beads. FIG. 42 shows a graph ofspectral response demonstrating the spectral match between the tissuesample and the scattering solution background 350.

Refer now to FIGS. 43 and 44, which schematically illustratealternative, roof background devices 360 in accordance with yet anotherembodiment of the present invention. The roof background devices 360make use of an optically transparent layer 362 such as a flat windowcomprising fused silica or MgF₂, a roof-like reflective diffuser 364,and a constituent layer 366 disposed therebetween. The opticallytransparent layer 362 may be used to surround and contain theconstituent layer 366. The constituent layer 366 may comprise water,collagen, lipid, or a mixture thereof. The diffuser 364 may include anirregular or otherwise non-planar surface such as roughened aluminum orstainless steel, or Spectralon of the proper reflecting characteristics.Light passes from the tissue sampler 12 through the window 362 andconstituent layer 366 to the diffuser 364. After undergoing a randomreflection from the diffusing surface, the light passes back through theconstituent layer 366 through the window 362 to the collection system14. FIG. 44 further illustrates the roof background device 360 disposedon a sampler interface 368 to which a cluster of fiber optic bundles 370is joined. Each fiber optic bundle preferably includes an arrangement ofa plurality of input and output fiber optic cables.

The parameters of the device 360 may be adjusted so that the collectedlight has similar spectral radiance to light that has interacted withtissue. FIG. 45 shows a graph of spectral response demonstrating thespectral match between the tissue sample and the roof background 360.The angles of the diffusing surface and the thickness of the water pathwere adjusted in simulation to achieve the theoretical result shown inFIG. 45. The spectral response of this system was calculated from thepathlength distribution and the known absorption spectrum of water. Itis important to note that the spectral match shown depends on adjustingthe mean energy of the background to match that of tissue.

Refer now to FIG. 46, which schematically illustrates a multi-layerbackground device 401 in accordance with a further embodiment of thepresent invention. The multi-layer background device 401 is based on amatch at discrete pathlengths to tissue. The multi-layer device 401includes an optically transparent window 410 such as an MgF₂ window, aplurality of optical splitting layers 420 such as partially reflectingquartz microslides, and a reflecting layer or surface 430 such as a goldmirror. Multiple constituent layers 440, such as water, are disposedbetween the window, 410, the optically transparent layers 420, and thereflective layer 430. The optically transparent window 410 may be usedto surround and contain the constituent layers 440. The diameter of themulti-layer background 400 is chosen to match the output area of thesampling optics for a given device.

Incident light from the tissue sampler 12 is broken up into componentswith discrete pathlengths by the optical splitting layers 420. Thereflectance of the optical splitting layers 420 and the thickness of theconstituent layers 440 may be adjusted in order to achieve the properdistribution of pathlengths in the device 401 so that a match to tissueis achieved. FIG. 47 shows a graph of spectral response demonstratingthe spectral match between the tissue sample and the multi-layeredbackground 401. For this test, the water layers 440 (labeled A, B, andC) were sized as follows: A=170 μm, B=205 μm, and C=150 μm. Themicroslide 420 between layer A and B had 4% reflectance, and themicroslide 420 between layer B and C had 32% reflectance. The goldmirror 430 had approximately 99% reflectance in the specified wavelengthregion

Refer now to FIG. 48, which schematically illustrates a transmissioncell background device 501 in accordance with yet a further embodimentof the present invention. The transmission cell background device 501also makes use of discrete constituent 520 pathlengths to match thepathlength distribution of tissue at key points. The transmission cellbackground device 501 includes an optically transparent container 510such as fused silica windows containing a plurality of spacers 530 suchas MgF₂ spacers to provide desired pathlengths. The remainder of thecontainer 510 is filled with a constituent 520 such as water. Thespacers function to displace the water or other constituent, creating abackground with several different length water paths. Suitabledimensions for the cell spacers are 0.226″, 0.216″, and 0.197″,respectively. These spacers may be used to create three water layerswith thickness values of 0.0098″, 0.0197″, and 0.0393″. The diameter ofthe transmission cell 501 is chosen to match the output area of thesampling optics for a given device. FIG. 49 shows a graph of spectralresponse demonstrating the spectral match between the tissue sample andthe transmission cell background 501. FIG. 49 indicates the degree ofmatch between the transmission cell (T-Cell) background 501 and thetissue sample to be on the order of+/−0.1 absorbance units.

The transmission cell background 501 may be incorporated into atransmission spectroscopy device by incorporating a second, reflectiveelement (not shown). The transmission cell described above is placedinto the optical beam of the spectrometer in a location such that thelight from the sampling optics passes through the transmission cellbefore being measured by the optical detector. A diffusely reflectingmaterial, such as Spectralon, is placed at the tissue sampler interfacein order to mimic the bulk scattering properties of tissue. This opticalsetup allows a similar background to be constructed that uses discretewater pathlengths in transmission to mimic the optical properties oftissue sampled using reflection sampling optics.

The transmission reference sample, as shown in FIG. 48, has threedifferent optical pathlengths. When examined by the multipath RMS errormetric over the region of 4200-7200 cm⁻¹, the magnitude of the residualclearly indicates the presence of multiple pathlengths throughgeneration of a value of approximately 0.11 absorbance units.

Refer now to FIG. 50, which schematically illustrates a variable heighttemporal background device 601 in accordance with another embodiment ofthe present invention. The temporal background device 601 includes anoptically transparent layer 610 and a movable diffuse reflector layer620, such as a Spectralon. A constituent layer 630 such as water isdisposed between the optically transparent layer 610 and the diffusereflector 620. The optically transparent layer 610 may be used tocontain the constituent layer 630 or a separate container 650 may beprovided for that purpose.

The temporal background device 601 uses a time-weighted samplingtechnique to produce proper throughput at various pathlengths that matchthe tissue path distribution. This, in turn, enables the spectral matchto tissue. A diffuse reflector 620 (approximately Lambertianhigh-reflectance material) is used to provide return illumination in theform of reflected light and is translated vertically (as shown by arrow640 and labeled h_(i)) to achieve a variable water path. The datapresented below were generated by varying the height of the Spectralonreflector 620 over the water layer h_(i) through values ranging from 0.1mm to 0.3 mm. The diameters of the MgF₂ window and Spectralon reflectorare chosen to match the output area of the sampling optics for a givendevice. Thus, the reflecting layer 620 is moved to a heightcorresponding to a given pathlength in the desired distribution, andlight is subjected to this pathlength and collected for a timeproportional to the weight of the particular path in the distribution.Upon combination of the time-sampled data, a match to the tissuespectrum can be achieved as shown in FIG. 51.

Refer now to FIG. 52, which schematically illustrates a collagen gelmatrix background device 700 in accordance with an embodiment of thepresent invention. The collagen gel matrix background device 700includes a container 710 that is partially optically transparent. Aconstituent 720 is disposed in the container and comprises a collagengel matrix. The collagen gel matrix may consist of denatured porcinecollagen in a gel state. Reflectance microbeads may be infused into thegel to create a randomized scattering path throughout the volume of theconstituent 720. For example, the collagen matrix 720 may be made from30% porcine gelatin, 0.8% 2 μm polystyrene beads, and 69.2% water. FIG.53 shows a graph of spectral response demonstrating the spectral matchbetween the tissue sample spectrum and the collagen gel matrixbackground spectrum 700. The actual gel thickness presented to thesampling system was 3.0 cm-4.0 cm. As can be seen from FIG. 53, a closematch to human tissue can be made if the proper preparation of thecollagen gelatin matrix is carried out, which can be accomplishedempirically. It is recognized that the gel matrix can be composed of anysubstance that enables an optically similar reference sample to becreated.

Refer now to FIG. 54, which schematically illustrates an animal basedbodily constituent (e.g., bovine tissue) background device 800 inaccordance with an embodiment of the present invention. The animal basedbodily constituent background 800 includes a container 810 that is atleast partially optically transparent and an animal (e.g., bovine,porcine) based bodily constituent 820 disposed therein. The animal basedbodily constituent may comprise an animal bodily tissue (e.g., skin), ananimal bodily fluid (e.g., blood) or other animal based biologicalconstituent. Through the use of a section of bovine tissue, a relativematch to human tissue is readily attained. The bovine tissue section maybe doped with analytes in order to simulate various in-vivoconcentration levels for humans. Because the spectral features of thebovine tissue section are similar to those found in human tissue, itprovides a good formulation of a tissue similar background for use incalibration maintenance. FIG. 55 shows a graph of spectral responsedemonstrating the spectral match between the tissue sample and thebovine tissue background 800. For the data shown in FIG. 55, 2 cm×4 cmrectangular sections of bovine collagen tissue approximately 1 cm thickwere used. The bovine collagen sample comprised a section of cowhideimmersed in distilled water to prevent dehydration.

In use as a subsystem of the present invention, any of the calibrationmaintenance devices having similar backgrounds discussed above isoptically coupled (e.g., positioned adjacent) to the illumination sourceand irradiated with multiple wavelengths of radiation from theillumination source. The collection system is used to collect radiationthat is not absorbed by the reference sample. The collected radiation isthen used to determine the intensities of the non-absorbed radiation ateach of the multiple wavelengths to generate a reference spectrum. A newcalibration model can be created or a pre-existing calibration model canbe modified based on the reference spectrum to account for instrumentand environment variations. Alternatively, the reference spectrum issimply used to alter a spectrum of a test sample to account forinstrument and environment variations without altering an existingmodel.

After the calibration model has been created or modified, a test sampleof interest is optically coupled (e.g., positioned adjacent) to thetissue sampler. The test sample (e.g., human tissue or blood) isirradiated with multiple wavelengths of radiation from the tissuesampler. Radiation that is not absorbed by the test sample is collectedby the tissue sampler collection system. The collected radiation is thenused to determine the intensities of the non-absorbed radiation at eachof the multiple wavelengths to generate a test spectrum corresponding tothe test sample of interest. In one embodiment, the newly created ormodified calibration model is used, and an analyte or attribute of thetest sample may be calculated based on the test spectrum. Alternatively,the test sample spectrum is modified based on the reference spectrum(i.e., a ratio or difference) and the modified test spectrum is usedwith an existing model to determine an analyte concentration orattribute.

Note that these steps may be reordered and/or modified without departingfrom the scope of the present invention. For example, the referencesample may have the same or separate interface with the instrument asthat used for the test sample of interest. Also, the reference samplemay have multiple components that are simultaneously measured atdifferent locations in the optical path of the spectroscopic instrument.Further, the reference sample may be manually or automaticallypositioned and measured.

In order to correct for the effects of instrument and environmentalvariation, the similar background is preferably sampled sufficientlyclose in time to the sample of interest. The required frequency ofsampling for the background is dependent on instrument stability andenvironmental variations which are being corrected. Preferably, abackground measurement is made just prior to measuring the sample ofinterest which allows the most current instrument state to bedetermined. In an alternative sampling scheme, the signal-to-noise ratioin the measured background spectrum is improved by taking multiplesimilar background measurements prior to measuring the sample ofinterest.

There are several schemes for optimizing the relationship between usingmultiple background sample measurements (higher signal-to-noise) andusing only the background sample measurement made closest in time to themeasurement of the sample of interest (most current instrument state).One such scheme is to use multiple, weighted, time-averaged backgroundsample measurements. Multiple background sample measurements arecollected over a period of time in order to increase the spectrum'ssignal-to-noise ratio. Weighted averaging allows those background samplespectra taken closest in time to the sample of interest to more heavilyinfluence the spectral correction.

There are multiple methods for using the spectral measurement of thesimilar background to correct for instrument and environmentalvariation. One simple and effective methodology is to ratio the measuredspectrum of the sample of interest to the measured spectrum of thesimilar background sample. This correction methodology removes spectralvariation that is common to both the similar background and the sampleof interest. This methodology may be used to both establish and maintaina multivariate calibration model, but in some cases, it is desirable touse this methodology only for calibration maintenance.

After generating a glucose prediction, the embedded computer subsystem600 will report the predicted value 830 to the subject. Optionally, theembedded computer subsystem 600 may report the level of confidence inthe goodness of the predicted value. If the confidence level is low, theembedded computer subsystem 600 may withhold the predicted glucose valueand ask the subject to retest. The glucose values may be reportedvisually on a display, by audio and/or by printed means. Additionally,the predicted glucose values will be stored in memory in order to form ahistorical record of the subject's glucose values over time. The numberof recorded glucose values is constrained only by the amount of memorycontained in the device.

The embedded computer subsystem 600 includes a central processing unit(CPU), memory, storage, a display and preferably a communication link.An example of a CPU is the Intel Pentium microprocessor. The memory canbe static random access memory (RAM) and/or dynamic random accessmemory. The storage can be accomplished with non-volatile RAM or a diskdrive. A liquid crystal display is an example of the type of displaythat would be used in the device. The communication link could be a highspeed serial link, an Ethernet link or a wireless communication link.The embedded computer subsystem produces glucose predictions from thereceived and processed interferograms, performs calibration maintenance,performs calibration transfer, runs instrument diagnostics, stores ahistory of measured glucose concentrations and other pertinentinformation, and in some embodiments, can communicate with remote hoststo send and receive data and new software updates.

The embedded computer system can also contain a communication link thatallows transfer of the subject's glucose prediction records and thecorresponding spectra to an external database. In addition, thecommunication link can be used to download new software to the embeddedcomputer, update the multivariate calibration model, provide informationto the subject to enhance the management of their disease, etc. Theembedded computer system is very much like an information appliance.Examples of information appliances include personal digital assistants,web-enabled cellular phones and handheld computers.

The present invention has been tested to show it achieves clinicallyrelevant levels of glucose prediction and accuracy over a minimum of twomonths for a diverse subject population. Using the non-invasive glucosemonitoring system depicted in FIG. 1, a glucose calibration model wasdeveloped on 40 subjects over a period of 6 weeks on 3 identicalinstruments. The calibration model was validated on 40 new subjects whowere not part of the calibration model. The 40-patient validation wasconducted over a period of 7 weeks, with each subject being measuredtwice per week. The results of the validation study are shown in FIG.56. FIG. 56 displays the correlation between the capillary blood glucosereference measurement (Yellow Springs Instruments 2700 Select) and theglucose concentration predicted by the non-invasive NIR quantitativespectroscopy measurement. This overall standard error of predictions forthe 40 subjects over the 7 weeks was 21.7 mg/dl. Further, 83.5% of theresults are within section A of the Clark Error Grid. This studydemonstrates non-invasive glucose measurements with clinicallyacceptable levels of accuracy and precision.

Those skilled in the art will recognize that the present invention maybe manifested in a variety of forms other than the specific embodimentsdescribed and contemplated herein. Accordingly, departures in form anddetail may be made without departing from the scope and spirit of thepresent invention as described in the appended claims.

What is claimed is:
 1. An apparatus for non-invasive measurement ofglucose in human tissue by quantitative near infrared spectroscopycomprising: an illumination subsystem which generates near infraredlight; a tissue sampling subsystem optically coupled to saidillumination subsystem which receives at least a portion of saidinfrared light, said tissue sampling subsystem including means forirradiating human tissue with at least a portion of said receivedinfrared light and collecting at least a portion of said light diffuselyreflected from said human tissue; a calibration maintenance subsystemselectively optically coupled to said tissue sampling subsystem forreceiving at least a portion of said infrared light and diffuselyreflecting a portion thereof; an FTIR spectrometer subsystem selectivelyoptically coupled to said tissue sampling subsystem to receive at leasta portion of said light diffusely reflected from said tissue orselectively optically coupled to said calibration maintenance subsystemto receive at least a portion of said infrared light diffusely reflectedtherefrom, said FTIR spectrometer subsystem including a spectrometerthat creates an interferogram, said FTIR spectrometer subsystem furtherincluding a detector which receives the interferogram and converts saidinterferogram to an electrical representation; a data acquisitionsubsystem which receives the electrical representation of theinterferogram, said data acquisition subsystem including means foramplifying and filtering said electrical representation and converting aresulting electrical signal to its digital representation; and acomputing subsystem for receiving said digital representation andfurther including means for determining glucose concentration in humantissue from said digital representation, wherein in combination saidsubsystems provide a clinically relevant level of glucose measurementprecision and accuracy.
 2. The apparatus of claim 1, wherein saidapparatus provides a clinically relevant level of glucose measurementprecision and accuracy, including 80% or more predictions on a singlesubject within a physiological range of glucose falling in the “A”region of a Clark Error Grid when compared to a reference measurement.3. The apparatus of claim 1, wherein said calibration maintenancesubsystem comprises a reference sample which receives a portion of saidinfrared light and reflects a portion thereof and produces a spectrumsimilar to a representative human tissue sample.
 4. The apparatus ofclaim 3, wherein the representative human tissue sample includesmultiple samples from multiple subjects.
 5. The apparatus of claim 4,wherein the reference sample has a spectral similarity ratio, whencompared with the representative human tissue sample spectra, of 30 orless over a spectral range of 4,200 cm⁻¹ to 7,200 cm⁻¹.
 6. The apparatusof claim 4, wherein the reference sample has a spectral similarityratio, when compared with the representative human tissue samplespectra, of 30 or less using discrete wavelengths, in wavenumbers (cm⁻¹)selected from the group consisting of: 4196, 4227, 4273, 4281, 4304,4320, 4335, 4366, 4389, 4436, 4451, 4459, 4497, 4528, 4559, 4613, 4690,4775, 4829, 4860, 4883, 4922, 5014, 5091, 5176, 5230, 5269, 5299, 5315,5338, 5369, 5392, 5454, 5469, 5477, 5515, 5585, 5623, 5662, 5701, 5731,5755, 5785, 5809, 5839, 5893, 5924, 5947, 6001, 6094, 6163, 6187, 6287,6318, 6349, 6449, 6472, 6557, 6595, 6673, 6696, 6935, 6973, 7004, 7043,7066, 7205, and combinations thereof.
 7. The apparatus of claim 4,wherein the reference sample has a spectral similarity ratio, whencompared with the representative human tissue sample spectra, of 30 orless over a spectral range of 4,440 cm⁻¹ to 4,800 cm⁻¹ and 5,440 cm⁻¹ to6,400 cm⁻¹.
 8. The apparatus of claim 4, wherein the reference samplehas a regression weighted spectral similarity ratio, when compared tothe representative human tissue spectra, of 30 or less over a spectralrange of 4,200 cm⁻¹ to 7,200 cm⁻¹.
 9. The apparatus of claim 4, whereinthe reference sample has a regression weighted spectral similarityratio, when compared to the representative human tissue spectra, of 30or less using discrete wavelengths, in wavenumbers (cm⁻¹) selected fromthe group consisting of: 4196, 4227, 4273, 4281, 4304, 4320, 4335, 4366,4389, 4436, 4451, 4459, 4497, 4528, 4559, 4613, 4690, 4775, 4829, 4860,4883, 4922, 5014, 5091, 5176, 5230, 5269, 5299, 5315, 5338, 5369, 5392,5454, 5469, 5477, 5515, 5585, 5623, 5662, 5701, 5731, 5755, 5785, 5809,5839, 5893, 5924, 5947, 6001, 6094, 6163, 6187, 6287, 6318, 6349, 6449,6472, 6557, 6595, 6673, 6696, 6935, 6973, 7004, 7043, 7066, 7205, andcombinations thereof.
 10. The apparatus of claim 4, wherein thereference sample has a regression weighted spectral similarity ratio,when compared to the representative human tissue sample spectra, of 30or less over a spectral range of 4,440 cm⁻¹ to 4,800 cm⁻¹ and 5,440 cm⁻¹to 6,400 cm⁻¹.
 11. The apparatus of claim 3, wherein the representativehuman tissue sample is from a single subject.
 12. The apparatus of claim11, wherein the reference sample has a spectral similarity ratio, whencompared with the representative human tissue sample spectra, of 1500 orless over a spectral range of 4,200 cm⁻¹ to 7,200 cm⁻¹.
 13. Theapparatus of claim 8, wherein the reference sample has a spectralsimilarity ratio, when compared with the representative human tissuesample spectra, of 1500 or less using discrete wavelengths, inwavenumbers (cm⁻¹) selected from the group consisting of: 4196, 4227,4273, 4281, 4304, 4320, 4335, 4366, 4389, 4436, 4451, 4459, 4497, 4528,4559, 4613, 4690, 4775, 4829, 4860, 4883, 4922, 5014, 5091, 5176, 5230,5269, 5299, 5315, 5338, 5369, 5392, 5454, 5469, 5477, 5515, 5585, 5623,5662, 5701, 5731, 5755, 5785, 5809, 5839, 5893, 5924, 5947, 6001, 6094,6163, 6187, 6287, 6318, 6349, 6449, 6472, 6557, 6595, 6673, 6696, 6935,6973, 7004, 7043, 7066, 7205, and combinations thereof.
 14. Theapparatus of claim 11, wherein the reference sample has a spectralsimilarity ratio, when compared with the representative human tissuesample spectra, of 7500 or less over a spectral range of 4,440 cm⁻¹ to4,800 cm⁻¹ and 5,440 cm⁻¹ to 6,400 cm⁻¹.
 15. The apparatus of claim 11,wherein the reference sample has a regression weighted spectralsimilarity ratio, when compared to the representative human tissuesample spectra, of 4500 or less over a spectral range of 4,200 cm⁻¹ to7,200 cm⁻¹.
 16. The apparatus of claim 11, wherein the reference samplehas a regression weighted spectral similarity ratio, when compared tothe representative human tissue sample spectra, of 3000 or less usingdiscrete wavelengths, in wavenumbers (cm⁻¹) selected from the groupconsisting of: 4196, 4227, 4273, 4281, 4304, 4320, 4335, 4366, 4389,4436, 4451, 4459, 4497, 4528, 4559, 4613, 4690, 4775, 4829, 4860, 4883,4922, 5014, 5091, 5176, 5230, 5269, 5299, 5315, 5338, 5369, 5392, 5454,5469, 5477, 5515, 5585, 5623, 5662, 5701, 5731, 5755, 5785, 5809, 5839,5893, 5924, 5947, 6001, 6094, 6163, 6187, 6287, 6318, 6349, 6449, 6472,6557, 6595, 6673, 6696, 6935, 6973, 7004, 7043, 7066, 7205, andcombinations thereof.
 17. The apparatus of claim 11, wherein thereference sample has a regression weighted spectral similarity ratio,when compared to the representative human tissue sample spectra, of 9000or less over a spectral range of 4,440 cm⁻¹ to 4,800 cm⁻¹ and 5,440 cm⁻¹to 6,400 cm⁻¹.
 18. The apparatus of claim 3, wherein the referencesample has a spatial similarity, expressed in terms of standarddeviation, of 0.079 or less.
 19. The apparatus of claim 3, wherein thereference sample has an angular similarity, expressed in terms ofstandard deviation, of 0.051 or less.
 20. An apparatus for non-invasivemeasurement of glucose in human tissue by quantitative near infraredspectroscopy comprising: an illumination subsystem which generates nearinfrared light, said illumination subsystem including a lighthomogenizer positioned to receive at least a portion of said infraredlight; a tissue sampling subsystem optically coupled to saidillumination subsystem which receives at least a portion of saidinfrared light exiting said light homogenizer, said tissue samplingsubsystem including means for irradiating human tissue with at least aportion of said received infrared light and collecting at least aportion of said light diffusely reflected from human tissue; an FTIRspectrometer subsystem selectively optically coupled to said tissuesampling subsystem to receive at least a portion of said light diffuselyreflected from said tissue, said FTIR spectrometer subsystem including aspectrometer that creates an interferogram, said FTIR spectrometersubsystem further including a detector which receives the interferogramand converts said interferogram to an electrical representation; a dataacquisition subsystem which receives the electrical representation ofthe interferogram, said data acquisition subsystem including means foramplifying and filtering said electrical representation and converting aresulting electrical signal to its digital representation; and acomputing subsystem for receiving said digital representation andfurther including means for determining glucose concentration in humantissue from said digital representation, wherein in combination saidsubsystems provide a clinically relevant level of glucose predictionprecision and accuracy.
 21. The apparatus of claim 20, wherein saidapparatus provides a clinically relevant level of glucose measurementprecision and accuracy, including 80% or more predictions on a singlesubject within a physiological range of glucose falling in the “A”region of a Clark Error Grid when compared to a reference measurement.22. The apparatus of claim 20, wherein said light homogenizer comprisesa light pipe.
 23. The apparatus of claim 22, wherein said light pipe hasa polygonal cross section.
 24. The apparatus of claim 20, wherein saidlight pipe includes one or more bends to achieve angular homogenization.25. The apparatus of claim 20, wherein angular homogenization isachieved, at least in part by passing the radiation through a glassdiffuser.
 26. The apparatus of claim 22, wherein said light pipeincludes a diffusely reflective coating on the interior surface thereof.27. The apparatus of claim 20, wherein said illumination subsystemfurther comprises a filament which generates said light and said lighthomogenizer sufficiently homogenizes said light so that light whichcontacts the human tissue has a spatial and angular distribution whichis repeatable through a one-millimeter vertical translation of thefilament resulting in a standard deviation of less than 0.053 in spatialdistribution and a standard deviation of less than 0.044 in angulardistribution.
 28. The apparatus of claim 20, wherein said illuminationsubsystem further comprises a light source including a filamentgenerating said light, wherein the light contacting the human tissue hasa spatial and angular distribution which is repeatable through aone-millimeter rotational translation of the filament resulting in astandard deviation of less than 0.050 in spatial distribution and astandard deviation of less than 0.066 in angular distribution.
 29. Theapparatus of claim 20, wherein the illumination subsystem includes alight source and the light homogenizer produces sufficient angular andspatial homogenization so that the inverse multivariate signal-to-noisevalue is about 60 or less when the light source is changed in theillumination subsystem.
 30. The apparatus of claim 20, wherein theillumination subsystem includes a light source that comprise atungsten-halogen lamp.
 31. The apparatus of claim 20, wherein said lightgenerated by said illumination subsystem possesses a band of wavelengthswithin the infrared regions of the electromagnetic spectrum.
 32. Theapparatus of claim 31, wherein the illumination subsystem furthercomprises means for concentrating the radiation emitted by the radiationsource emitter.
 33. The apparatus of claim 20, wherein the samplingsubsystem comprises means for channeling at least a portion of the lightexiting the light homogenizer to the human tissue.
 34. The apparatus ofclaim 33, wherein the channeling means is at least one fiber optic wire.35. The apparatus of claim 33, wherein the channeling means is at leastone mirror.
 36. The apparatus of claim 33, wherein the channeling meansis at least one optic lens.
 37. An apparatus for non-invasivemeasurement of glucose in human tissue by quantitative near infraredspectroscopy comprising: an illumination subsystem which generates nearinfrared light including means for angularly and spatially homogenizingat least a portion of said light; a tissue sampling subsystem opticallycoupled to said illumination subsystem which receives at least a portionof said infrared light, said tissue sampling subsystem including meansfor irradiating human tissue with at least a portion of said receivedinfrared light and collecting at least a portion of said light diffuselyreflected from said human tissue, said tissue sampling subsystemincluding at least one input element which transfers said light to saidhuman tissue and at least one output element which receives light fromsaid tissue, wherein said input element and said output element arespaced apart by a gap of about 100 μm or greater; a calibrationmaintenance subsystem selectively optically coupled to said tissuesampling subsystem for receiving at least a portion of said infraredlight and diffusely reflecting a portion thereof, said calibrationmaintenance subsystem including a reference sample having opticalproperties similar to a representative human tissue sample; an FTIRspectrometer subsystem selectively optically coupled to said tissuesampling subsystem to receive at least a portion of said light diffuselyreflected from said tissue or selectively optically coupled to saidcalibration maintenance subsystem to receive at least a portion of saidinfrared light diffusely reflected therefrom, said FTIR spectrometersubsystem including a spectrometer that creates an interferogram, saidFTIR spectrometer subsystem further including a detector which receivesthe interferogram and converts said interferogram to an electricalrepresentation, said detector that is sensitive to light in the 1.2 to2.5 μm region of the spectrum; a data acquisition subsystem with aminimum SNR of 100 dbc which receives the electrical representation ofthe interferogram, said data acquisition subsystem including means foramplifying and filtering said electrical representation and ananalog-to-digital converter for converting the resulting electricalsignal to its digital representation; and a computing subsystem forreceiving said digital representation and further including means fordetermining glucose concentration in human tissue from said digitalrepresentation, wherein in combination said subsystems provide aclinically relevant level of precision and accuracy.
 38. The apparatusof claim 37, wherein said apparatus provides a clinically relevant levelof glucose measurement precision and accuracy, including 80% or morepredictions on a single subject within a physiological range of glucosefalling in the “A” region of a Clark Error Grid when compared to areference measurement.
 39. The apparatus of claim 37, wherein saiddetector is a thermo-electrically cooled, extended range InGaAs detectorthat is sensitive to light in the 1.2 to 2.5 μm region of thespectrum.33.
 40. The apparatus of claim 37, wherein said input elementand said output element comprise, at least in part, optical fibers. 41.The apparatus of claim 40, wherein said optical fibers have ends pottedinto a cluster ferrule which is mounted in said sampling head.
 42. Theapparatus of claim 37, wherein said cradle includes a base having anopening therethrough in which said sample head is disposed.
 43. Theapparatus of claim 42, wherein said means for positioning human tissuerelative to said sampling surface comprises a bracket extending upwardfrom said base which references an elbow of a subject's arm disposedthereon.
 44. The apparatus of claim 43, wherein said cradle furtherincludes an adjustable hand rest spaced longitudinally from said bracketalong said base.
 45. The apparatus of claim 44, further including meansfor raising and lowering said cradle to form and reform the tissueinterface.
 46. The apparatus of claim 37, wherein the input elementsurface area is at least seven times greater than the output elementsurface area.
 47. An apparatus for non-invasive measurement of glucosein human tissue by quantitative near-infrared spectroscopy comprising:an illumination subsystem which generates near-infrared light; a tissuesampling subsystem optically coupled to said illumination subsystemwhich receives at least a portion of said infrared light generated bysaid illumination subsystem, said tissue sampling subsystem includingmeans for irradiating human tissue with at least a portion of saidreceived infrared light and collecting at least a portion of said lightdiffusely reflected from human tissue, said means for irradiating humantissue including at least one input element which transfers said lightto said human tissue and at least one output element which receiveslight from said tissue; an FTIR spectrometer subsystem selectivelyoptically coupled to said tissue sampling subsystem to receive at leasta portion of said light diffusely reflected from said tissue, said FTIRspectrometer subsystem including a spectrometer that creates aninterferogram, said FTIR spectrometer subsystem further including adetector which receives the interferogram and converts saidinterferogram to an electrical representation, said detector that issensitive to light in the 1.2 to 2.5 μm region of the spectrum; a dataacquisition subsystem which receives the electrical representation ofthe interferogram, said data acquisition subsystem including means foramplifying and filtering said electrical representation and converting aresulting electrical signal to its digital representation; and acomputing subsystem for receiving said digital representation andfurther including means for determining glucose concentration in humantissue from said digital representation, wherein in combination saidsubsystems provide a clinically relevant level of precision andaccuracy.
 48. The apparatus of claim 47, wherein said detector is athermo-electrically cooled, extended range InGaAs detector that issensitive to light in the 1.2 to 2.5 μm region of the spectrum.
 49. Theapparatus of claim 47, wherein said apparatus provides a clinicallyrelevant level of glucose measurement precision and accuracy, including80% or more predictions on a single subject within a physiological rangeof glucose falling in the “A” region of a Clark Error Grid when comparedto a reference method.
 50. An apparatus for non-invasive measurement ofglucose in human tissue by quantitative near-infrared spectroscopycomprising: an illumination subsystem which generates near-infraredlight; a tissue sampling subsystem optically coupled to saidillumination subsystem which receives at least a portion of saidinfrared light generated by said illumination subsystem, said tissuesampling subsystem including means for irradiating human tissue with atleast a portion of said received infrared light and collecting at leasta portion of said light diffusely reflected from human tissue, saidmeans for irradiating human tissue including at least one input elementwhich transfers said light to said human tissue and at least one outputelement which receives light from said tissue; an FTIR spectrometersubsystem selectively optically coupled to said tissue samplingsubsystem to receive at least a portion of said light diffuselyreflected from said tissue, said FTIR spectrometer subsystem including aspectrometer that creates an interferogram, said FTIR spectrometersubsystem further including a detector which receives the interferogramand converts said interferogram to an electrical representation; a dataacquisition subsystem with a minimum SNR of 100 dbc which receives theelectrical representation of the interferogram, said data acquisitionsubsystem including means for amplifying and filtering said electricalrepresentation and converting a resulting electrical signal to itsdigital representation and an analog-to-digital converter for convertinga resulting electrical signal to its digital representation; and acomputing subsystem for receiving said digital representation andfurther including means for determining glucose concentration in humantissue from said digital representation, wherein in combination saidsubsystems provide a clinically relevant level of precision andaccuracy.